Bioinformatics is full of opportunities. The sector is poised to open new avenues for the other related sectors also. But the biggest opportunity area in the Bioinformatics market will be in the drug discovery sector. Reduction of both the cost and time taken to discover a new drug due to fast development in the Bioinformatics tools and software zone is also making drug discovery an attractive field to venture in. Malaysian bioinformatics growth and future are discuss in this https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723929/ paper. Keeping all such inportance in mind, following universities in Malaysia offering bioinformatics cources:

3 program(s) at AIMST University, Malaysia

Master of Science in Biotechnology (MSc) - Bioinformatics by Research

Master of Science (M.Sc) in Medical Microbiology (Bioinformatics) by Research

Doctor of Philosophy in Biotechnology (PhD) - Bioinformatics by Research

1 program(s) at INTI International University and Colleges, Malaysia

American Degree Transfer Program (Biosciences) in Bioinformatics

3 program(s) at Management and Science University (MSU), Malaysia

Master in Bioinformatics (By Research)

PhD in Bioinformatics

Bachelor in Bioinformatics (Hons)

1 program(s) at Multimedia University (MMU), Malaysia

Bachelor of Science (Honours) Bioinformatics

1 program(s) at Universiti Industri Selangor (UNISEL) Bestari Jaya Campus, Malaysia

Bachelor of Bioinformatics (Hons)

2 program(s) at Universiti Malaysia Sabah (UMS), Malaysia

PhD - Doctor of Philosophy in Bioinformatics (By Research)

MSc - Master of Science in Bioinformatics (By Research)

6 program(s) at Universiti Putra Malaysia (UPM), Malaysia

MSc - Master of Science in Bioinformatics by Research

Master of Science in Bioinformatics and System Biology by Research

Master of Science (M.Sc) in Bioinformatics and Systems Biology (With Thesis)

PhD - Doctor of Philosophy in Bioinformatics by Research

PhD - Doctor of Philosophy in Bioinformatics and Systems Biology (With Thesis)

PhD - Doctor of Philosophy in Bioinformatics and System Biology by Research

1 program(s) at Universiti Selangor (UNISEL), Malaysia

Bachelor of Bioinformatics (Hons)

3 program(s) at Universiti Teknologi Malaysia (UTM), Malaysia

M.Sc - Master of Science (Bioscience) in Bioinformatics Research Group (BIRG) By Research

PhD - Doctor of Philosophy (Bioscience) in Bioinformatics Research Group (BIRG) By Research

Bachelor of Computer Science (BioInformatics)

4 program(s) at University of Malaya (UM), Malaysia

MSc - Master of Science in Bioinformatics by Research

Master in Bioinformatics by Coursework

PhD - Doctor of Philosophy in Bioinformatics by Research

Bachelor of Science (BSc) in Bioinformatics

3 program(s) at Perdana University, Malaysia

Master in Bioinformatics (By Research)

PhD in Bioinformatics

Bachelor in Bioinformatics (Hons)

3 program(s) at Monash University, Malaysia

Master in Bioinformatics (By Research)

PhD in Bioinformatics

Bachelor in Bioinformatics (Hons)

The real bioinformatics scope lies if there are research labs which work in this field. One has to take account of that. If so then try to get information of those labs and visit them to get a hang of the work they pursue. For detail Bioinformatics in Malaysia: Hope, Initiative, Effort, Reality, and Challenges are discussed in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723929/ paper.

Starting with your current or most recent employment provide details of your position as follows:

• A chronological CV should be arranged in reverse chronological order. It should be apparent immediately where you are now.
• Remember that an application form is limited to the few things that a particular institution wants to know about everybody. A CV lets you give information that is unique to you. Add all your key accomplishments and activities in the initial draft. In subsequent drafts, you can remove information that may not be pertinent.
• Resist the temptation to append explanatory sentences or language, which will distract the reader from the basic information being presented. The language of a CV is abbreviated and succinct. When applying for residency training, you will have the opportunity to express yourself in a personal or biographical statement. In the future, when applying for a job or some other type of position, you will want to include an appropriate cover letter with your CV to explain your particular qualifications and strengths for the position.
• Don’t despair if your CV doesn’t resemble those of other students who are applying to the same residency program. Everybody’s CV is different. Even if everyone used the same format suggested in this section, your CV will not resemble others’ because it doesn’t have the same content. No residency program director is looking for a specific CV style. You will receive points for neatness, and readability.
• Be honest. If you haven’t accomplished anything in a particular category, leave it out. Don’t create accomplishments to fill in the spaces. You can be specific about your level of participation in a project or activity, but don’t be misleading (i.e., you can say you coordinated membership recruitment for your AMSA chapter, but don’t say you were “president” unless you were).

• Job title - time that you have held this position
• The key tasks and responsibilities that comprise this role's requirements - notable achievements whilst in the role
• Where possible quantify your achievements with precise facts and figures, e.g. managed junior staff, handled department budget, prepared management reports
• Expand on the skills you are using in your current job which you believe will be valuable in the position(s) for which you are applying

It is not necessary to state the reason you are leaving your current position. This will be a topic for conversation when you are invited for interview or can be covered in your letter of application.
For all previous employment, unless one appointment was more significant than your current or last position, keep details brief i.e. the name of the company, job title, period of employment and the job.
Be sure there are no gaps in your career history - unless for example you took a year out to travel, in which case make reference to this under Interests/Hobbies.
If you are a student just starting work, give any evidence you can to demonstrate your practical skills e.g. school prefect, event organisation, member of sports team, contributor to college magazine or voluntary work.

You are under no obligation to disclose marital status, age or whether or not you have children unless these are specific criteria for selection for a position that you are interested in.
Consider what examples (interests/ hobbies) you can give to show that you match the selection criteria.

If they want someone to work as part of a large team, remember to say if you belong to a local organisation or if you are part of a sports team.

If they want someone who will work on their own for large periods of time, make reference to an Open University course you are considering undertaking.

Your primary objective is to convince the prospective employer that you have the requisite skills, experience and hunger to do the job.

Your CV should be no more than two A4 pages and as every employer is different remember to customise your CV to every job you go after.

There are abundant books on the contents and presentation of a general CV. A BMJ article published in 1978, offering doctors guidance on how to prepare a CV, has been reprinted in the widely read How to do it series.1 2 A survey among postgraduate deans and training advisers at regional colleges found that the contents and presentation of a model CV for doctors in training has been published.3 It is perhaps surprising to note that these models differ significantly from one another. Although they may be useful as starting points, their differences tend to create confusion and anxiety among students. I would argue that these differences exist because the content and presentation of the "ideal" CV vary considerably among individual applicants, the stage of their careers, and the purposes for which the CV is used. It is impossible to create a generic CV. I have therefore not attempted to draw up another model CV. Rather, the purpose of this article is to outline the general principles and important practical points in preparing a good CV. General principles on contents Before finalising your CV for a particular purpose you must be sure of your objectives, whether it is used as an initial screening or the only selection instrument, and the criteria against which it is judged. What details, and how many of them, to include in your CV depends on these factors. I shall illustrate with examples relevant to medical students. 

(1) Job application Your objective is to get the job. In a job application, the CV is used for two purposes: as an initial screening instrument for shortlisting candidates and as a framework for discussion during the interview. The explicit criteria used for shortlisting are usually given in the job advertisement. For some organisations, separate lists of essential and desirable criteria are given. Alternatively, you can get a good idea of the basic requirements from the job description. Your CV must clearly highlight these criteria, preferably on the first page. These usually include: formal qualifications; registration with the General Medical Council; and the prescribed experience. It is sometimes easy to forget to mention items specifically asked for in the job description (for example, a valid driving licence). The implicit criteria are less easy to pinpoint. For example, how much detail on your BSc dissertation and publications should you include? Should you make a long list of extracurricular activities, interests outside medicine, and positions of responsibility? If you admit to a wide range of extracurricular activities and interests, would you be considered as a candidate with a well balanced mature personality or will it be interpreted to mean that you will have little time or interest to do your job? There are no easy answers. Common sense might tell you that BSc dissertation and publications are more important in application for teaching hospital or research posts, but less important for district hospital posts. Conversely, you might think that interests outside medicine are more important for posts in district hospitals or in general practice. This is, however, not always true. A few consultants at district hospital are highly academic. Information gathered from students and doctors working under the professor or consultant concerned may be vital. Alternatively, it is worth while doing your homework by looking up your prospective consultant in the medical directory. This may occasionally prompt you to include information that you might otherwise have left out. For example, you may find that the consultant qualified in Scotland and has previously worked in Scotland for a considerable time, and you may decide to add in your CV that you studied in a Scottish secondary school. Since the CV is only used as for initial screening, you need not go into your previous experience or extracurricular activities in too much detail. Highlight only the most significant points, and leave the details for the interview. If you are applying for a clinical post, one of your referees should be a consultant for whom you have worked as a student. You should ask for permission to use his/her name before submitting your application. 

(2) Application for research scholarship or PhD studentship The CV and application form are sometimes used as the sole selection instrument, and you must make enquiries before you submit your application. Clearly, academic ability is the main criterion for selection, and you should include as much information relevant to your academic ability and interests as you can. Examples are your A levels, your BSc dissertation, any publications (even in the form of a letter in newspaper), any experience as an editor (for example, for your school magazine). Your extracurricular activities are less important, and you can simply give a short list. At least one of the referees should be an academic - for example, your previous supervisor in your BSc degree.
 
(3) Application to join a clinical course in another medical school Preclinical students who have completed an intercalated degree often have the option of applying to join a clinical course in another medical school, although it may become more difficult to do so with the introduction of the new GMC curriculum. Your CV is usually used for shortlisting candidates for interview. The criteria differ slightly among medical schools, but both academic ability and contribution to university life are important. Hence, not only should you highlight your academic achievements but you must also highlight your participation in the university (for example, in sports or music). 

(4) For the information of your tutor or counsellor For most medical schools, you are allocated a tutor who provides both academic and non-academic support and monitors your progress throughout your study on a confidential basis. Students are sometimes asked to submit their up to date CV to their tutors for information. Assuming that the tutors are helpful there is little to gain from over emphasising your strengths or hiding your weaknesses. Once you become a doctor in training after you qualify you will need to undergo an annual assessment of your progress. It serves to certify that you have reached a satisfactory standard, but it is also used as an aid to identify and help with your weaknesses. You may find it difficult to balance these two purposes in presenting your CV. General principles on presentation Now that all students are computer literate, there should be few problems in preparing a well presented CV. The following list serves as a reminder on how to present your CV effectively: Spelling or grammar mistakes - do not rely purely on the spell check on your computer. Ask friends to proofread your CV for you. Consistency - The use of punctuation to open and close sentences, justification, and fonts should be consistent. Readability - The headings should be clear. The font size should be no less than 12 point. Basic criteria - The basic criteria should be easily located, preferably on the first page. Length - The length of your CV increases as you progress up the professional ladder. For students, it should generally be no more than three pages. Quality of print - The CV should be printed on good quality paper, preferably using a laser printer. Practical points In this article, I argue that different versions of a CV may be required for different purposes. Even applications for different posts in the same specialty may require slightly different versions. Also, CVs need to be updated regularly. This would have been time consuming to achieve in the past, but it is now quite simple, with the aid of a basic word processing package. A master CV containing all relevant information should be prepared and saved as a computer file. This should be continuously kept up to date. When the need for a CV arises, it can be tailormade by editing the master document. It is important to save each of these edited versions separately, with the file names indicating the date when it was created and the purpose. It is also important to prepare a cover letter to go with the CV. Key messages A good CV is essential for successful progression up the medical professional ladder The contents of the CV should be tailormade for the purpose it is used for and the criteria against which it is judged. It is important to gather information about these criteria first The CV must be technically well presented, with the basic criteria easily located Information technology has made it simple to regularly update our CVs and allows preparation of different versions of a CV for different purposes with relative ease.

Reference:

Prepare a curriculum vitae. BMJ 1978;25(2):1478-9.
O'Brien E. Prepare a curriculum vitae. In: Reece D, ed. 
How to do it. Vol 1. London: BMJ Publishing Group, 1995 Chambler AF, Chapman-Sheath PJ, Pearse MF. 
A model curriculum vitae: what are the trainers looking for? Hosp Med 1998;59(4):324-6.

Pondicherry University Puducherry has been identified as a nodal agency by the Department of Biotechnology, Govt. of India to coordinate this examination along with nine centres namely, Pune University, Pune; Anna University, Chennai; Calcatta University (WBUT) Kolkata; Institute of Bioinformatics & Applied Biotechnology, Bangalore; North-Eastern Hill University, Shillong, University of Hyderabad, Hyderabad; University of Kerala, Thiruvananthapuram; Jawaharlal Nehru University, New Delhi and Assam Agricultural University, Guwahati.

In the BINC 2013 examination,17 candidates were certified. DBT has agreed to fund Research fellowships for all the BINC qualified Indian nationals to pursue Ph.D. in Indian Institutes/Universities. Note that the candidate must possess a postgraduate degree(or equivalent) & meet the criteria of the institutes/universities in order to avail research fellowship. In addition, cash prize of Rs. 10,000/- will be awarded to the top 10 BINC qualifiers.

More at http://210.212.230.224:9999/BINC/

1. Strand NGS
   • offers many different tools including alignment, RNA-Seq, DNA-Seq, ChIP-Seq, Small RNA-Seq, Genome Browser, visualizations, Biological Interpretation, etc. Supports workflows “one can import the sample data in FASTA, FASTQ or tag-count format. In addition, prealigned data in SAM, BAM or Illumina-specific ELAND format can be directly imported for analysis.”
   • Alignment feature: Supports alignment from Illumina, Ion Torrent, 454 (Roche), and Pac Bio
   • DNA-Seq Feature, can annotate with dbSNP
2. CLC Genomics Workbench
   
   • (QIAGEN). Features include: resequencing, workflow, read mapping, de novo assembly, variant detection, RNA-Seq, ChIP-Seq, Genome Browser, etc (entire list on website); Main Workbench offers database search (Genbank, Blast, Pubmed); 2000 organizations have invested in CLC
   • Accepts VCF files from 1000 Genomes Project
   • Accepts downloaded tracks from dbSNP
   • Also accepts: FASTA, GFF/GTF/GVF, BED, Wiggle, Cosmic, UCSC variant database, complete genomics master var file
   • Read mapping: “In addition to Sanger sequence data, reads from these high-throughput sequencing machines are supported: The 454 FLX System and the 454 GS Junior System from Roche, Illumina Genome Analyzer, Illumina HiSeq, Illumina HiScan, and Illumina MiSeq sequencing systems, SOLiD system from Life Technologies, Ion Torrent system from Life Technologies, Helicos from Helicos BioSciences”
   • De novo assembly: “In addition to Sanger sequence data, reads from these high-throughput sequencing machines are supported The 454 FLX System and the 454 GS Junior System from Roche, Illumina Genome Analyzer, Illumina HiSeq, Illumina HiScan, and Illumina MiSeq sequencing systems, SOLiD system from Life Technologies, Ion Torrent system from Life Technologies”
   • Annotation tracks from Ensembl
3. DNAnexus
   • Private cloud repository -- formerly a redistributor of SRA and other NCBI resources; command-line or via web, can fetch data from a URL, build custom pipeline/ workflow has sra.dnanexus.com site: data downloads come directly from NCBI
4. Ingenuity Variant Analysis
   • (QIAGEN) allows for variant identification and analysis, uses NCI-60 data set for cancer, Supported third part informatin: Entrez Gene, RefSeq, ClinVar; gives contextual details of results instead of just A to B relationship
   • Has own database-- “knowledge base” based on COSMIC, OMIM, and TCGA databases
5. Lasergene Genomics Suite
   • Comprehensive NGS software pipeline for assembly, alignment, variant calling and analysis of NGS data
   • Supported workflows include: reference-guided and de novo genome and transcriptome assembly and analysis, metagenomics sample assembly, targeted resequencing, exome alignment, gene panels with validation control, variant analysis, and RNA-Seq, ChIP-Seq and miRNA alignment and analysis.
   • #1 in accuracy: fewer false negatives and better sensitivity compared to results obtained from other aligners
   • Aligns exome data and performs variant calling an average of 3 times faster than alternative pipelines
   • Annotates genomic data with allele and genotype frequency, functional impact predictions, evolutionary conservation scores and pathogenicity
   • Supports all major NGS technologies (Illumina, Ion Torrent, Pac Bio and Roche 454) and project types
   • Available on Windows, Mac OS X, Linux, and the Amazon Cloud
6. NextGENe
   • “perfect analytical partner for the analysis of desktop sequencing data produced by the ION PGM™, Roche Junior, Illumina MiSeq as well as high throughput systems as the Ion Torrent Proton, Roche FLX, Applied BioSystems SOLiD™ and Illumina® platforms.” runs on Windows, free-standing multi-application package-- SNP/Indel analysis, CNV prediction and disease discovery, whole genome alignment, etc.
   • Data can be imported from Clinvar, dbSNP, Genbank:http://www.softgenetics.com/PDF/NextGene_UsersManual_web.pdf
7. Partek Genomics Suite
   • Cited in over 3,500 peer-reviewed scientific publications
   • Workflows for microarray and PCR data include: Gene expression including alternative splicing, miRNA expression, Genome Wide Association Studies, Mother-Father-Child Trio analysis, DNA Copy number including allele specific copy number and Loss of Heterozygosity (LOH), and ChIP, and methylation. Next Generation Sequencing (NGS) workflows include: RNA-Seq, miRNA-Seq, ChIP-Seq, DNA-Seq, and Methylation
   • Powerful statistics and interactive, publication ready visualizations
   • Supports all commercial next generation sequencing and microarray file format as well as text files
   • Can input GEO SOFT files
8. Partek Flow
   • Installation can be cloud-based or on a local cluster or Linux server
   • Easy to use point-and-click interface
   • Takes NGS data (.fastq, BAM, SAM), microarrays (Affymetrix, Illumina) and text files
   • Supports custom genome builds and annotation databases
   • Performs base trimming, alignment, quantification, quality analysis, statistics, and visualization
   • Includes ten fully customizable aligners (Bowtie, Bowtie 2, BWA, GSNAP, Isaac 2, SHRiMP 2, STAR, TMAP, TopHat and TopHat 2)
   • Applications for RNA-Seq, Small RNA-Seq, WGS/WES, Pathway enrichment, Fusion detection and Variant calling
   • Allows users to create, save, share, or download analysis pipelines for automated and repeatable analysis
   • Collaborate with others without transferring data
   • Integrates microarray and next generation sequencing data
9. Golden Helix: SNP and Variation Suite
   • used for managing, analyzing and visualizing genotypic and phenotypic data; Features: Genome-wide association studies, genomic prediction, copy number analysis, small sample DNA-Seq workflows, large sample DNA-seq analysis, RNA-seq analysis. Supported files: .txt, excel XLS & XLSX, CEL, CHP, CNT, Illumina, Plink PED, TPED, BED, Agilent files, NimbleGen data summary files, VCF files, Impute2 GWAS files, HapMap format, MACH output, + 50 other formats consumes NCBI data directly
10. Genomatix
    • Applications: ChIP-Seq, DNA-Seq, RNA-Seq, DNA methylation; enable personalized medicine,
    • Mining Stations: Supports all established NGS sequencing platforms- SOLiD, 454 Life Sciences, Genome Analyzer, HiSeq, MiSeq, IonTorrent
    • Software Suite: can upload sequence of BED files
    • Genome browser: BED and BAM files, Public data- 1500 BED files available for every user
11. Biodatomics
    • Open source platform (SaaS), analysis and genome sequencing tools, integrates over 400 genomic analysis open source tools and pipelines, have a private and public cloud version. Features: genomic data visualization, drag and drop interface, accelerated analysis, real-time collaboration
    • They have a couple modules to do so, and have enabled parts of the sra toolkit
12. SolveBio
    • Software product, for clinical genomics professionals, manage, curate, report genomic variation
    • Has own data library -- data from NCBI
13. Basepair
    • Offers high quality workflows for all common NGS applications (RNA-Seq, ChIP-Seq, DNA-Seq, etc.)
    • Very fast - get all results in a 1-2 hours. Cloud-based, no storage or computing limits.
    • Easy to use - less than a minute to run an analysis
    • REST and Python API to mange large projects.
     

Variant Identification

Germline Callers

1. IMPUTE2
   • Description: phasing observed genotypes and imputing missing genotypes uses reference panels to provide all available halotypes, does not use population labels or genome-wide measures; designed to represent variation in one population; Fairly popular
   • Input:
   • Reference Haplotypes: Links to 1000 Genomes and HapMap downloads
   • Output:
2. FreeBayes
   • Description: finds SNPs, Indels, MNPs; reports variants based on alignment; haplotype based
   • Input: BAM- uses BAMtools API to parse
   • Reference genome: FASTA
   • Output: VCF
3. SOAPindel
   • Description: detects indels from NGS paired-end sequencing
   • Input: files with read alignment can be SOAP or SAM formats, users must also give raw reads in Fasta or Fastq
   • Reference Sequence used to align reads: FASTA
   • Output:
4. 2Kplus2
   • Description: algorithm searches graphs produced by de novo assembler Cortex; c++ source code for SNP detection “2kplus2.cpp is a c++ source code for the detection and the classification of single nucleotide polymorphisms in transformed De Bruijn graphs using Cortex assembler.”
   • Input:
   • Output:
5. Atlas 2
   • Description: specializes in separation of true SNPs and indels from sequencing and mapping errors, last update January 2013
   • Input: takes BAM file,
   • Reference Genome: FASTA
   • Output: produces VCF
6. CRISP
   • Description: identifies SNPs and INDELs from pooled high-throughput NGS, not used for analysis of single samples; implemented in C and uses SAMtools API; latest version should work with diploid genomes
   • Input: requires BAM files (aligned with GATK)
   • Reference Genome: indexed FASTA file
   • Output: VCF files
7. Dindel
   • Description: (Wellcome Trust Sanger) calls small indels from short-read sequences, only can handle Illumina data; cannot test candidate indels; written in C++, used on Linux based and Mac computers (not tested in windows)
   • Input: BAM files
   • Output: VCF
8. discoSnp++
   • Description: detects homozygous and heterozygous SNPs and Indels; software composed of 2 modules (kissnp2 and kissreads)
   • Input: raw NGS datasets; fasta, fastq, gzipped or not;
   • no reference genome required; read pairs can be given
   • Output: FASTA
9. FamSeq
   • Description: family-based sequencing studies- provides probability of an individual carrying variant based on family’s raw measurements; accommodates de novo mutations, can perform variant calling at chrX;
   • Input: VCF
   • Output: VCF
10. GeneticThesaurus
    • Description: “Annotation of genetic variants in repetitive regions”
    • Input: Initial variant calling from bam → vcf output
    • Reference Genome: need to provide own fasta file for hg19 genome,
    • Output: vcf.gz, vtf.gz, and baf.tsv.gz output
11. glfMultiples
    • Description: command-line, variant caller
    • Input: GLF
    • Output: VCF
12. glfSingle
    • Description: uses likelihood-based model for variant calling, starts from genotype likelihoods that have been computed from other tools (ex. Samtools BAQ), the likelihoods combine with individual-based prior p(genotype) to generate posterior probabilities
    • Input: GLF
    • Output: VCF
13. Halvade
    • Description: command-line; written in Java, “to run halvade a reference is needed for both GATK and BWA and a SNP (dbSNP!) database is required
    • Input: FASTQ
    • Output: VCF
14. indelMINER
    • Description: identifies indels from paired-end reads
    • Input: BAM (aligned in SAMtools API)
    • Output: VCF
15. Indelocator
    • Description: (Broad Institute): does not perform realignment, relies on alignments in BAM files (BAM files need aligned before put into indelocator); recommended to use GATK prior;
    • Input: 2 BAM files(tumor & normal), annotated as germline or somatic; also has single sample mode
    • Output: “Output of Indelocator is a high-sensitivity list of putative indel events containing large numbers of false positives. The statistics reported for each event have to be used to custom-filter the list in order to lower false positive rate”
16. Isaac Variant Caller
    • Description: detects SNPs and small indels from diploid sample; designed to run on “nux-like platforms”
    • Input: BAM
    • Output: VCF
17. KvarQ
    • Description: in silico genotyping for selected loci in bacterial genome, written in Python and C
    • Input: FASTQ
    • reference genome or de novo assembly not needed
    • Output:
18. LoFreq
    • Description: SNV caller, Python language, standalone program, uncovers cell-population heterogeneity from high-throughput sequencing datasets; calls variants found in <.05% of the population
    • Input: BAM file input→ suggest running through GATK
    • Output:
19. Manta
    • Description: Calls indels and SVs from paired end reads; standalone, command line program; Written in C++ and Python
    • Input: BAM (can tolerate non-paired-end reads); a matched tumor sample may be provided as well
    • Output: VCF
20. MarginAlign
    • Description: SNV caller, specifically tailored to Oxford Nanopore Reads, written in Python; Package comes with 3 programs, marginAlign, marginCaller (calls SNVs), marginStats (computes qc stats on sam files)
    • Input: SAM
    • Output: SAM
21. MendelScan
    • Description: Last release March 2014; for analyzing sequencing data in family studies of inherited diseases; variant calls for a family in VCF file; still in alpha-testing on github, example data uses 1000 genomes dataset
    • Input:
    • Output:
22. nanopore
    • Description: UCSC Nanopore group (group at UCSC studying using ion channels for analysis of single RNA/DNA structures) software pipeline; tailored to Oxford Nanopore Reads; command line program
    • Input: FASTQ
    • Reference files: FASTA
    • Output: “For each possible pair of read file, reference genome and mapping algorithm an experiment directory will be created in the nanopore/output directory.”
23. Platypus
    • Description: Package program, written in C, Python, Cython; Can identify SNPs, MNPs, short indels, and larger variants; has been tested on very large datasets (1000 genomes)
    • Input: BAM
    • Reference Genome: FASTA (files must be indexed using Samtools or similar program
    • Output: VCF
24. QualitySNPng
    • Description: detection of SNPs; “can be used as a standalone application with graphical user interface as part of pipeline system”; does not require fully sequenced reference genome; haplotype strategy
    • Input:SAM, ACE
    • Output: GUI
25. ReviSTER
    • Description: command line program; automated pipeline; utilizes BWA, BLAT, and SAMTools; utilizes BWA mapping program;
    • Input: FASTQ,
    • Reference sequence file and list file containing STR locations as inputs
    • Output: SAM
26. RVD
    • Description: command-line program, detection of rare SNVs, relies upon Samtools, can be run in MATLAB
    • Input: BAM
    • Reference Genome: FASTA
    • Output: “The algorithm output is a call table -- a comma-separated file with one line for each base position and each line in the following format:
    • AlginmentReferencePosition, AlignmentBase, Call ,SecondBase, CenteredErrorPrc, ReferenceErrorPrc, SecondBasePrc”
27. SNVer
    • Description: calls common and rare variants in pool or individual NGS data, reports overall p-value, operating system independent statistical tool, identifies SNPs and INDELs, written in Java, no dependencies, straightforward command-line
    • (SNVerGUI=GUI version) --SNVerGUI: desktop tool for variant detection
    • Input: chrX annotation, sam.zip, bam.zip
    • reference file must be aligned to the data file
    • Output:
28. SNVMix
    • Description: detects SNVs from NGS, post-alignment tool
    • Input: pileupformat (Maq or Samtools)
    • Output:
29. SV-M
    • Description: Structural Variant Machine - predicts indels, uses split read alignment profiles, validated by Sanger Sequencng
    • Input:paired-end Illumina reads from 1001 genomes project (uses ref plant- 1001genomes.org)
    • Ouptut:
30. SNPest
    • Description: Standalone program, language C++, Perl
    • Input: mpileup (SAMtools)
    • Output: VCF
31. TrioCaller
    • Description:Command line program, relies on BWA and samtools; genotype calling for unrelated individuals and parent-offspring trios
    • Input: BAM (that has been aligned in BWA and Samtools
    • Output: BCF that can be formatted to VCF using bcftools
32. Snippy
    • Description: finds indels between haploid reference genome and NGS sequence reads
    • Input:read files- FASTQ or FASTA (can be .gz compressed), output- .aln, .tab, .txt
    • Reference genome in FASTA or GENBANK
    • Output:
33. VntrSeek
    • Description: pipeline for discovering microsatellite tandem repeats with high-throughput sequencing data
    • Input: gzip-compressed FASTA or FASTQ
    • Output: VCF files; one for TRs and observed alleles, another file contains link to viewer

Somatic Callers

1. Cake
   • Description: standalone program, “pipeline for the integrated analysis of somatic variants in cancer genomes”; integrates four algorithms; written in Perl; required tools: samtools, tabix, vcftools, VarScan2, bambino, cmake, somaticsniper (User guide; workflow page)
   • Input: tumor and normal reads in BAM files, run through variant calling programs to generate intermediate VCF
   • Output: VCF
2. MuTect
   • Description: Broad Institute, identification of somatic point mutations in cancer genomes; requires preprocessing of reads (GATK)
   • Input: same as GATK (FASTA reference genome, SAM read files)
   • Output: call-stats, VCF, wiggle files
3. Polymutt
   • Description: calls SNVs and detects de novo point mutations in families
   • Input: GLF or BAM or VCF (must have identical chromosome orders)
   • Output: VCF
4. Bassovac
   • Description: Improved Bayesian inversion somatic caller; unlike other software packages, treats effects fully probabilisticallys instead of using ad-hoc modeling; effects are integrated at the atomic level and standard probability theory integrates read tallies to the sample level and to the tumor-normal pair level; "pending public release"
   • Input:
   • Output:
5. CLImAT
   • Description: standalone program; “accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole genome sequencing data”
   • Input: depth file generated by DFExtract and a config file
   • Output: .results file, .Gtype, LOG.txt, also generates visualization
6. DeNovoGear
   • Description: de-novo variant calling and interpretation; standalone program; dependencies C++ compiler, CMake, HTSlib, Eigen, Boost
   • Input: PED and BCF
   • Output: “The output format is a single row for each putative de novo mutation (DNM), with the following fields”
7. EBCall
   • Description: Empirical Baysian Mutation Calling; standalone program; uses tumor/normal paired reads and non-paired normal reference samples; dependent on samtools, R and VGAM pack for R
   • Input: BAM
   • Output: not sure what exact type of file- “The format of the result is suitable for adding annotation by annovar.”
8. HapMuc
   • Description: standalone program; “utilizes the information of heterozygous germline variants near candidate mutations”; Dependent upon- Boost, SAMtools, BEDtools; 3 step workflow
   • Input: BAM
   • Output: BED
9. MultiGeMS
   • Description: Multi-sample Genotype Model Selection
   • Input: .txt, pileup (SAM/BAM converted to pileup format)
   • Output: VCF
10. MultiSNV
    • Description: command-line program; calls SNVs from NGS data from multiple samples from the same patient; dependent on R, Git, cmake, Boost and compile libraries
    • Input: BAM or pileup
    • Output: VCF
11. MutationSeq
    • Description: standalone program, somatic SNV detection in tumor/normal samples; dependent on python, bamtools, boost, and LAPACK
    • Input: BAM
    • Output: VCF4.1 consisting of two parts (meta information & data lines)
12. qSNP
    • Description: standalone program; SNV caller for somatic variants in “low cellularity cancer samples”
    • Input: BAM, dbSNP data, Illumina data, chrConv
    • Output: “qSNP output files are named using a 4-element pattern: ...”
13. RADIA
    • Description: RNA and DNA Integrated Analysis for Somatic Mutation Detection; DNA only Method(tumor/normal pair, ignores RNA) or Triple BAM Method (uses all three datasets from same patient); dependent upon python, samtoools, pysam API, BLAT, SnpEff
    • Input: BAM
    • Reference Genome: FASTA indexed with SAMtools faidx
    • Output: VCF
14. RVD2
    • Description: sensitive, variant detection for low-depth targeted NGS data; python module or command- line program;
    • Input: tab- deliminted depth chart format (converted from pileup files)
    • Output: three hdf5 files and a vcf file
15. Shimmer
    • Description: standalone program; detects somatic SNVs with multiple testing correction, uses Fisher’s exact test; dependent on git, samtools, R, R statmod package; for tumor/normal matched samples
    • Input: BAM
    • Output: VCF
16. SNV-PPILP
    • Description: Refines GATK’s Unified Genotyper SNV calls for “multiple samples assumed to form a phylogeny”
    • Input:
    • Output:
17. SomaticSniper
    • Description: command-line application to identify SNPs between tumor/normal pairs- predicts probability of difference between two
    • Input: BAM
    • Reference Genome in FASTA
    • Output: VCF
18. Strelka
    • Description: somatic variant calling workflow for matched tumor-normal samples; detects indels; runs on *nux-like platform
    • Input: BAM (must be sorted and indexed)- Strelka does own realignment around indels-- don’t need to do this type of pre-processing
    • Output: pair of VCF files
19. Triodenovo
    • Description: Bayesian framework for calling de novo mutations in trios
    • Input: VCF file with PL or GL fields (recommend using GATK or samtools to generate)
    • Output: out_vcf
20. UNCeqr
    • Description: finds somatic mutations using integration of DNA and RNA seq data-- boosts sensitivity for low purity tumors and rare mutations;
    • Input:”can accept a variety of sequencing inputs and configurations”
    • Output: “table of somatically mutated sites and associated information. These somatic mutations can be annotated with predicted transcript and protein effects using third party tools, such as Annovar”
21. Virmid
    • Description: Virtual Microdissection for SNP calling; Java based; for disease-control matched samples; uncovers SNPs with low allele frequency by considering alpha contamination
    • Input: BAM (must be sorted and indexed- samtools sort)
    • Output: VCF and report file

Germline + Somatic Callers

1. VarScan 2
   • Description: identify germline variants, private and shared variants, somatic mutations, and somatic CNVs; detects indels
   • Input: SAMtools pileup
   • Output: VCF
2. BAYSIC
   • Description: Bayesian method; combines variant calls from different methods (GATK, FreeBayes, Atlas, Samtools, etc)
   • Input: VCF format from one or more variant calling programs
   • Output: VCF file containing integrated set of variant calls
3. MSIsensor
   • Description: Microsatellite instability detection; C++ program, detects somatic and germline variants in tumor-normal paired data
   • Input: BAM index files (normal and tumor)
   • Output:
4. Beagle version 4
   • Description: software package: genotype calling, phasing, imputation of ungenotyped markers, and identity-by-descent segment detection:unsure if this one is in the right category; genotype calling, phasing, imputation of ungenotyped markers, and identity-by-descent segment detection;
   • Input: VCF
   • Output: VCF
5. QuadGT
   • Description: software package, SNV calling from normal-tumor pair and two parent genomes; quantifies descent-by-modification relationships; Written in Java
   • Input: BAM files (parsed by Picard/Samtools API)
   • Reference Genome; FASTA
   • Output: VCF
6. RAREVATOR
   • Description: RAre REference VAriant annotaTOR; command line; “identification and annotation of germline and somatic variants in rare reference allele loci from second generation sequencing data”; Bayesian genotype likelihood model
   • Input: BED or VCF files from GATK
   • Output: two VCF files (one for SNVs, one for Indels)
7. Scalpel
   • Description: Used for detecting indels in a reference genome; performs localized micro-assembly of specific regions of interest; can do single, de novo, somatic reads; requires that raw reads are aligned with BWA
   • Input: BAM
   • Output: either VCF or ANNOVAR
8. SOAPsnp
   • Description: based on Baye’s theorem; calls consensus genotype
   • Input:SOAP short read alignment results
   • Output: GLF, option of flat tabular format
9. VariantMaster
   • Description: “extract causative variants for monogenic and sporadic genetic diseases”; uses ANNOVAR;
   • Input: BAM or VCF files (from SAMtools, GATK)
   • Output:

Downstream Analysis of Variants

1. PrediXcan
   • Description: command-line, standalone package program; available in Perl, Python, and R versions; predicts liklihood of a gene being related to a certain phenotype- “that directly tests the molecular mechanisms through which genetic variation affects phenotype.”; no actual expression data used, only in silico expression; “PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.”
   • Input: genotype and phenotype file (doesn’t specify file type)
   • Output:default values: genelist, dosages (file format: snpid rsid) , dosage_prefix, weights, output
2. ATHENA
   • Description: Analysis Tool for Heritable and Environmental Network Associations; software package, combines machine learning model with biology and statistics to predict non-linear interactions
   • Input: Configuration file, Data file, Map file (includes rsID)
   • Output: Summary file, Best model file, dot file, individual score file, cross-validation file
3. CCRaVAT and QuTie
   • Description: (Wellcome Trust Sanger) Case-Control Rare Variant Analysis Tool and Quantitative Trait; software packages for large-scale analysis of rare variants
   • Input: PED file and MAP file
   • Output: Five tab-delimited txt files
4. GCTA
   • Description: Genome Wide Complex Trait Analysis; package program, command line interface; estimates variance by all SNPs; 5 main functions: “data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation”
   • Input: PLINK binary PED files, MACH output format
   • Output:
5. GenomeComb
   • Description: package for analysis of complete genome data; annotation using public data or custom tracks, automated primer desing for Sanger or Sequenom validation; “The cg process_illumina command can be used to generate annotated multisample data starting from fastq files, using tools such as bwa for alignment and GATK and samtools for variant calling. Sequencing data can also be imported from Complete Genomics (cg_process_sample command), Real Time Genomics (cg_process_rtgsample command) and VariantCallFormat (VCF) variant files (vcf2sft command).”
   • Input: Sequencing data from Complete Genomics, Illumina, SOLiD and VCF;
   • Output: standard file format used is a simple tab delimited file (.sft, .tsv)
6. Genome Track Analyzer
   • Description: compares genome tracks; allows user to compare DNA expression/binding;
   • Input: multiple: SGR/TXT, BED, BED6, GFF; if using prealigned sequence data- use MACS peak caller: BAM, BED, SAM, ELAND
   • Output:
7. GVCBLUP
   • Description: animal gene mapping; “genomic prediction and variance component estimation of additive and dominance effects”; standalone program, command line interface, writting in C++ and Java
   • Input:
   • Output:
8. HOMOG
   • Description: Analyzes heterogeneity with respect to single marker loci or known maps of markers; Carries out homogeneity test for alternative hypothesis “Two family types, one with linkage betweeen a trait to a marker or map of markers, the other without linkage”
   • Input: HOMOG.DAT - described on website
   • Output: HOMOG.OUT
9. INTERSNP
   • Description: GWIA for case-control SNP and quantitative traits; selected for joint analysis using priori information; Provides linear regression framework, Pathway Association Analysis, Genome-wide Haplotype Analysis,
   • Input: PLINK input formats (ped/map, tped/tfam, bed/bim/fam) Compatible with SetID files
   • Gene reference file: Ensembl Release 75
   • Output: covariance matrix for regression models
10. mtSet
    • Description: Currently only the standalone version available, but moving to LIMIX software suite; offers set tests- allows for testing between variants and traits; accounts for confounding factors ex. relatedness
    • Input: sample-to-sample genetic covariance matrix needs to be computed; multiple types of input; simulator requires input genotype and relatedness component;
    • Output: resdir (result file of analysis), outfile (test statistics and p-values), manhattan_plot (flag)
11. MultiBLUP
    • Description: Package program, command line interface; constructs linear prediction models; Best Linear Unbiased Prediction; improves upon BLUP involving kinship matrices; options: pre-specified kinships, regional kinships, adaptive multiblups, LD weightings
    • Input: PLINK format
    • Output:.reml, .indi.blp

Variant Annotation

1. ANNOVAR
   • Description: command-line tool, supports SNPs, INDELs, CNVs and block substitutions, provides wide variety of annotation techniques, depends upon multiple databases (each needing to be downloaded); annotates genetic variants; utilizes RefSeq, UCSC Genes, and the Ensembl gene annotation systems; can compare mutations detected in dpSNP or 1000 Genomes Project; Very popular *“The final command run TABLE_ANNOVAR, using dbSNP version 138, 1000 Genomes Project 2014 Oct version, NIH-NHLBI 6500 exome database version 2 (referred to as esp6400siv2), dbNFSP version 2.6 (referred to as ljb26), dbSNP version 138 (referred to as snp138) databases and remove all temporary files, and generates the output file called myanno.hg19_multianno.txt”
   • Input: VCF, ANNOVAR input format (simple text-based format); can convert other formats into ANNOVAR input format
   • Output: VCF (if input VCF), output file with multiple columns, tab-delimited output file
2. wANNOVAR
   • provides web-based access to ANNOVAR software
3. PolyPhen-2
   • Description: Very popular; Polymorphism Phenotyping; Web application; predicts impact of amino acid substitution on protein; Calculates Bayes posterior probability (Last update July 2015)
   • Input: FASTA
   • Output:
4. SIFT
   • Description: predicts how an amino acid substitution will affect protein function; Based on degree of conservation of amino acid residues- collected though PSI-BLAST; can be applied to nonsynonymous polymorphisms or laboratory-induced missense mutations; links to dbSNP 132, GRCh37; Standalone or web app program; Very popular
   • Input: Uniprot ID or Accession, Go term ID, Function name, Species Name or ID, etc
   • Output:
5. snpEff
   • Description: Genetic variant annotation and effect prediction toolbox; integrated with Galaxy, GATK, and GNKO; can annotate SNPs, INDELs, and multiple-nucleotide polymorphisms; categorizes effects into classes by functionality; Very popular; Standalone or Web app; Claims to calculate all SNPs in 1000 genomes (EMBI) in less than 15 minutes; can annotate SNPs, MNPs, and insertions and deletions; Provides assessment of impact of the variant ( low, medium or high)
   • Input: VCF, BED
   • Output: VCF (with new ANN field, also used in ANNOVAR and VEP), HTML summary files
6. SnpSIFT
   • Description: Filter and manipulate annotated files; Part of SnpEff main distribution; one variants have been annotated, this can be used to filter your data to find relevant variants
   • Input:
   • Output:
7. VAAST 2
   • Description: Variant Annotation, Analysis, and Search Tool; probabilistic search tool for identifying damage genes and the disease causing variants; can score both coding and non-coding variants; Four tools: VAT (Variant annotation tool), VST (Variant Selection Tool), VAAST, pVAAST (for pedigree data); updated April 2015
   • Input: FASTA, GFF3, GVF
   • Output: CDR (condenser file), VAAST file (both unique to VAAST)
8. VEP
   • Description: (Ensembl) Variant Effect Predictor; determines effect of variants on genes, transcripts, and protein sequence; uses SIFT and PolyPhen
   • Input: Coordinates of variants and nucleotide changes; whitespace- separated format, VCF, pileup, HGVS
   • Output: VCF, JSON, Statistics
9. ABSOLUTE
   • Description: (Broad Institute); can estimate purity and ploidy to compute absolute copy number and mutation multiplicitie; reextracts data from the mixed DNA population
   • Input: HAPSEQ segdat or segmentation file
   • Output: per-sample output directory and subdirectory providing per-sample text files containing standard out being emitted from R
10. Alamut Batch
    • Description: high-throughput annotation software for NGS analysis; for “intensive variant analysis workflows”; “enriches raw NGS variants with dozens of attributes”; based on clinically oriented Alamut database; Supports human genes; easy to integrate into pipeline (Latest Release- July 2015)
    • Input:VCF, tab-delimted file
    • Output: tab-separated file of annotations
11. AVIA
    • Description: Annotation, Visualization, and Impact Analysis; “The tool is based on coupling a comprehensive annotation pipeline with a flexible visualization method. We leveraged the ANNOVAR (Wang et. al, 2010) framework for assigning functional impact to genomic variations by extending its list of reference annotation databases (RefSeq, UCSC, SIFT, Polyphen etc.) with additional in-house developed sources (Non-B DB, PolyBrowse).”
    • Input: BED
    • Output: Table of annotations with gene annotation features
12. BioR
    • Description: (Mayo Clinic) (Page last updated June 2015) Biological Reference Repository; “data integration tool that enables coordinate based searches and joins based on strings”; “BioR consists of two parts 1) the BioR toolkit which depends on Java…. 2) the BioR catalogs which are the data files used by the system”
    • Input: VCF
    • BioR-Supported Catalogs (tar-gzip files): dbSNP, 1000 genomes, HapMap, OMIM, NCBIGene
    • Output: VCF + JSON
13. CADD
    • Description: Combined Annotation Dependent Depletion; tool for scoring SNV deletions/insertions; “integrates multiple annotations into one metric”; Score strongly correlates with allelic diversity and pathogenicity; links to 1000 Genome variants; uses Ensembl Variant Effect Predictor
    • Input: VCF
    • Output: CADD score
14. CandiSNPer
    • Description: web application, characterizes SNPs located in vicinity of SNP of interest;
    • Input: enter SNP ID (rsID), choose population, region, measure for LD, threshold plot format, color of SNPs, and chose to show genes
    • Output: Imagefile
15. CanvasDB
    • Description: “local database infrastructure for analysis of targeted- and whole genome re-sequencing projects”; dependent on MySQL, R, and ANNOVAR
    • Input:
    • Output:
16. CAROL
    • Description: (Wellcome Trust Sanger); Combined Annotation scoRing toOL; Combined functional annotation score of nonsynonymous coding variants; Combines information from PolyPhen-2 and SIFT
    • Input: tab-delimited with columns obtained from PolyPhen-2 and SIFT output
    • Output: tab-delimited file
17. CHASM
    • Description: Cancer-specific High-throughput Annotation of Somatic Mutations; Last updated May 2014; uses Random Forest Method to “distinguish between driver and passenger somatic mutations”; Positive driver class curated from COSMIC database; packed together with SNVBox (database)
    • Input:Passenger mutation rates, Transcript and amino acid change, Genomic coordinates
    • Output: CHASM score, p-value, FDR
18. CRAVAT
    • Description: Cancer-Related Analysis of Variants Toolkit; Web application; Uses CHASM, VEST, SNVGet; “CRAVAT provides predictive scores for germline variants, somatic mutations and relative gene importance, as well as annotations from published literature and databases” Latest Release May 2015;
    • Input: VCF, CRAVAT format
    • Output: CRAVAT report- MS Excel spreadsheet or tab-separated file (emailed)
19. CUPSAT
    • Description: Cologne University Protein Stability Analysis Tool; “tool to predict changes in protein stability upon point mutations”; web service program; Can predict mutant stability from existing PDB structures or custom protein structures
    • Input:for PDB- provide PDB ID and Amino Acid Residue Number; for custom- PDB file format
    • Output:
20. DANN
    • Description: Deleterious Annotation of genetic variants; standalone program, uses “the same feature set and training data as CADD to train a deep neural network”; can catch nonlinear relationships; “There are four different datasets: training, validation, testing, and ClinVar_ESP...The ClinVar_ESP dataset is also a testing set containing a set of “gold standard” pathogenic and benign variants”
    • Input:
    • Output:
21. ESEfinder
    • Description: Exonic Splicing Enhancer; useful for interpretation of point mutations/polymorphisms that are disease-associated; GUI interface; web app program
    • Input: FASTA
    • Output: html or plain text format, graphical display of results
22. Exomiser
    • Description: Wellcome Trust Sanger; functionally annotates variants from whole-exome sequencing data; Based on Jannovar and uses UCSC KnownGene; Java program; web app program (Page last modified Feb 2015)
    • Input: VCF
    • Output: TSV, VCF
23. FamAn
    • Description: Automated variant annotation pipeline for family-based sequencing studies; Annotaties SNVs and INDELs; 4 models- autosomal dominant, autosomal recessive, de novo mutations and a general model; “A variety of annotations are provided for each segregating variant: number of family (and family ID) each variant hits, variant genomic location and coding effect (based on snpEff), loss-of-function mutation annotation, selected ENCODE annotation, allele frequency in the 1000 Genomes Project, allele frequency in the Exome Variant Server (ESP6500), segmental duplication annotation, SIFT, PolyPhen2, LRT, MutationTaster, GERP++, PhyloP, SiPhy, etc.” (Last updated May 2014)
    • Input: VCF
    • Output: two excel compatible outputs
24. GeneTalk
    • Description: Combines tool for filtering and data analysis with an online network for genetic professionals; Different degrees- basic license, premium license, in-house solution (the last ones are paid for- Commercial tool?)
    • Input: VCF
    • Output: GeneTalk Annotation- includes clinical data, medical relevance, scientific relevance (http://www.gene-talk.de/public/GeneTalk_Whitepaper_Annotations.pdf)
25. GeneVetter
    • Description: “GeneVetter is a tool designed for investigation of the background prevalence of exonic variation in the Phase 3 1000 Genomes data under user defined filtering criteria”; web app program; GeneVetter uses GRch37p4 (hs37d5.fa.gz), dbSNP build 138, 1000G Phase 3, clinvar_2014072
    • Input: VCF
    • Output: TIMS score, summary table, PCA plot
26. GSITIC
    • Description: (Broad Institute) Last update- July 2014; Identifies genomic regions that are significantly “amplified or deleted”; Each is given a G score; gives genomic locations and q-values from aberrant regions
    • Input: segmentation file -seg, markers file -mk (required); -array file list -alf, CNV file -cnv
    • Reference genome: -refgene (created in MATLAB, GISITIC provides four reference genomes: hg16.mat, hg17.mat, hg18.mat, hg19.mat
    • Output: All lesions file (text file), amplifications file (text file), deletion genes file (text file), Gistic Scores file, Segmented copy number (pdf file), amplification score GISTIC plot (pdf file), Deletion score/q-vale GISTIC plot (pdf file)
27. HOPE
    • Description: Have yOur Protein Explained; Web app program; Automatic mutant analysis server that provides structural effects of a mutation; Uses BLAST against UniProt and PDB along with homology modeling
    • Input: FASTA protein sequence, or accession code of protein of interest
    • Output: a report containing information from a “decision tree” and illustrated figures and animations
28. Human Splicing Finder
    • Description: Last update: May 2013; aimed to help study pre-mRNA splicing; combines 12 algorithms to identify mutations’ effect on splicing motifs; uses ensembl database 70
    • Input: Gene Name, Ensembl transcript ID, Ensembl Gene ID, Consensus CDS, RefSeq Peptide ID, or own sequence (looks like you can enter FASTA)
    • Output: Chart with columns for predicted signal, predicted algorithm, cDNA position and interpretation
29. LARVA
    • Description: Large-scale Analysis of Variants in noncoding Annotations; New version released July 2015; Command-line program; used for studying noncoding variants; integrates comprehensive set of noncoding elements, modeling their mutation count; Dependent on C++ and BEDtools
    • Input: multiple
    • Output:
30. LINKAGE
    • Description:three main programs: mlink (calculates lod scores at fixed values for the recombination fraction in one interval of a genetic map), linkmap (calculates location scores for positions of a disease locus along a marker), and ilink (estimates parameters including recombination fractions, allele frequencies, penetrances, etc)
    • Input: pedfile (processed by MAKEPED) and datafile (reflects loci for each individual; set in PREPLINK)
    • Output:
31. MAC
    • Description: MNV Annotation Corrector; Ad hoc software, fixes incorrect amino acid predictions that are caused by multiple nucleotide variations; Uses existing annotators ANNOVAR, SnpEff, VEP (last update April 2015) (only 1 download this week → not popular)
    • Input: List of called SNVs and corresponding BAM
    • Output: Report identifying block of mutation within codon (BMCs)
32. mit-o-matic
    • Description: focuses on mtDNA, provides clinically relevant information from different resources; two component pipeline: command link for alignment of NGS reads and online version that provides genetic report on mitocondrial variants
    • Input:FASTQ, pileup
    • Reference sequence: rCRSm
    • Output: Online version gives comprehensive genetic report
33. Mutadelic
    • Description: Web App program; “This application generates reports on inherited mutations in five genes (ANK1, SLC4A1, SPTA1, SPTB and EPB42) associated with the following rare Mendelian blood disorders: Hereditary Spherocytosis (HS), Hereditary Elliptocytosis (HE) and Hereditary Pyropoikilocytosis”; Newer program- recently validated on omictools
    • Input: Can upload coordinates of DNA variants or VEP
    • Output: Displayed on web or can be downloaded in Excel or RDF format
34. MutationTaster
    • Description: (Last post on site 2014) Web app program; Rapid evaluation of disease causing alterations; uses NCBI 37 and Ensembl 69
    • Input: HGNC symbol, NCBI GeneID, or Ensembl ID,
    • Output: Report containing prediction, summary, name of alteration, etc
35. MutPred
    • Description: web app tool; Classifies amino acids substituation as disease associated or neutral in humans; Last modified Feb. 2014; Based on SIFT, trained using Human Gene Mutation Database
    • Input:
    • Output: “The output of MutPred contains a general score (g), i.e., the probability that the amino acid substitution is deleterious/disease-associated, and top 5 property scores (p), where p is the P-value that certain structural and functional properties are impacted.”
36. MutSigCV
    • Description: (Broad Institute) Mutation Significance (CV= covariates); Analyzes mutations discovered in DNA sequencing to identify genes that were mutated more often than expected
    • Input: mutations.maf, coverage.txt, covariates.txt
    • Output: output.txt
37. NGS-SNP
    • Description: Collection of command-line scripts for providing rich SNP annotations; “NCBI, Ensembl, and Uniprot IDs are provided for genes, transcripts and proteins when applicable”;
    • Input: Samtools consensus pileup, Maq, diBayes, Genetic format, VCF
    • Output: File containing annotated SNPs is copied from SNP list and some classes are added
38. Oncotator
    • Description: (Broad Institute) “Tool for annotating human genomic point mutations and data relevant to cancer researchers”; Web app; Supports annotation of data from ClinVar, dbSNP, 1000 genomes (plus many other external sites); Only GRCh27 coordinates supported; Last update: April 2015
    • Input: tal-delimited file
    • Output: tab-delimited MAF
39. PANTHER
    • Description: Protein ANalysis THrough Evolutionary Relationships; Web app program, also has its own database; Classification system used to classify proteins and their genes; Also, “Estimates the likelihood of a particular nonsynonymous (amino-acid changing) coding SNP to cause a functional impact on the protein”; Updated in 2015
    • Input: Data from PANTHER, IDs from Ensembl, EntrezGene, NCBI GI numbers, NCBI UniGene IDs HUGO, UniProt; if ID type is not one of the above, can input txt file or excel format
    • Output: Analysis results displayed online
40. PESX
    • Description: Putative Exonic Splicing Enhancers/Silencers; (Can’t tell if this is outdated or not)
    • Input: FASTA or plain text
    • Output: Excel spread sheet
41. Phen-Gen
    • Description: Combines patient's’ disease symptoms with sequencing data; Standalone or Web app version; Only excepts 1 family per run, in order to evaluate unrelated individuals, each sample needs to be run individually
    • Input: Variant- VCF; Pheotype- HPO; Pedigree- PED
    • Output: Combined scores file, variants for top genes file
42. PMUT
    • Description: Aimed at annotation and prediction of pathological mutations; based on different kinds of sequence info and neural networks to process information
    • Input: FASTA
    • Output; Simple yes/no and reliability index
43. PROVEAN
    • Description: Protein Variation Effect Analyzer; predicts whether an amino acid substitution or indel has impact on biological function of the protein; “comparable to SIFT or Polyphen-2”; Standalone, Web app, Command line or GUI; Last update May 2014
    • Input: FASTA, list of variants;
    • Output: tab-separated columns including Variant, Provean Score and prediciton
44. Rescue-ESE
    • Description: “An online tool for identifying candidate ESEs in vertebrate exons”; Web application; For human, mouse, zebrafish, pufferfish
    • Input: multi-FASTA or plain text
    • Output:
45. SCAN
    • Description: Web application program, includes a database as well; Database contains physical-based SNP annotations and functional annotations; “Information on physical, functional, and LD annotation served on the SCAN database comes directly from public resources, including the HapMap (release 23a), NCBI (dbSNP 129), or is information created by us using data downloaded from these public resources”; “SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms”
    • Input:
    • Output: HTML, comma-delimited, tab-delimited
46. SeattleSeq Annotation
    • Description: “SeattleSeqAnnotation137 was most recently updated October 13, 2013. The current version is 8.08. The most recent site, based on dbSNP build 141, and hg38/NCBI 38”; Provides annotations for SNVs and Indels- includes dbSNP rsID, gene names and accession numbers, variation functions, protein positions and amino acid changes, conservation scores, HapMap frequencies, PolyPhen predictions and clinical association.
    • Input: Maq, gff, CASAVA, VCF, GATK bed, custom
    • Output: “default output file format is a header line (starting with "#") followed by tab-separated annotations”; VCF
47. seqminer 3.7
    • Description: “Efficiently Read Sequence Data (VCF Format, BCF Format and METAL Format) into R”; Command line package program; Published August 2015
    • Input: VCF, BCF
    • Output: VCF
48. SG Adviser
    • Description: Scripps Genome Annotation and Distributed Variant Interpretation Server, web developed applications for variant annotation, “Downstream applications of variant annotation include: Clinical sequencing applications including: carrier testing, or identification of causal variants in molecular diagnosis, tumor sequencing, or diagnostic odyssey. Prioritization of variants prior to statistical analysis of sequence based disease association studies, especially for automated set-generation and enrichment of likely functional variants within sets. Identification of causal variants in post-GWAS/linkage sequencing studies. Identification of causal variants in forward genetic screens (stay tuned for non-human annotation)”
    • Input: SNV- VCF, BED, and a few others; CNV- BED, CNVator, plus others
    • Output: tab-delimited file
49. SNAP-2
    • Descriptio

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Bii.a-star.edu.sg

Bio research and development. Has course information and research information.

Isb-sib.ch

SIB operates the ExPASy proteomics server and the Swiss node of EMBnet. Teaching activities include a series of post-graduate courses given at the Universities of Geneva and Lausanne, as well as at the EPFL, and a Masters Degree in bioinformatics. Major research areas include the development of integrated databases and software resources in the field of proteomics.

Bioinformatics.ca

Provides information about bioinformatics in Canada. Workshops, certification and resources.

Chickscope.beckman.uiuc.edu

Students raise chicken embryos in the classroom and obtain magnetic resonance images through the Internet.

Bcb.iastate.edu

Graduate program at Iowa State University offering Undergraduate Major (BCBio) and the PhD program (BCB).

Bu.edu/bioinformatics/

Interdisciplinary PhD and Masters Programs that include an internship in the local industry companies. In conjunction with the NE masters program.

Bioinformatics.ubc.ca

A computational biology research centre covering many areas of genomics, proteomics, computer science and statistics. Research, training, news and events, resources and support, director's message, faculty and personnel.

Openhelix.com

Provides onsite training on specific bioinformatics databases and tools. Also offers bioinformatic software testing and research consulting services.

Igb.uci.edu

Specializing in making publicly available software and database services for computational biology.

Bioinformatics.pe.kr

Maintained by Dr. Seyeon Weon, Korea providing information on courses, a database archive, software archive and online resources.

Groups.yahoo.com/group/bimatics/

Bioinformatics group for students interested and/or working in the bioinformatics/computationalbiology fields. Offers opportunities to exchanging information and sharing ideas.

Ncbi.nlm.nih.gov/books/NBK22183/

Information about several medically important genes and related diseases. Illustrates the use of bioinformatics in their study.

Bioinfo.mbb.yale.edu/mbb452a/2003/

Bioinformatics course at Yale University. All course slides are available online.

Cs.iastate.edu/~honavar/comp-bio-courses.html

Listing of computational molecular biology course pages that have extensive online course materials.

Bioinf.manchester.ac.uk/dbbrowser/bioactivity/prefacefrm.html

A web-based tutorial associated with "Introduction to bioinformatics" published by Addison Wesley Longman.

Northeastern.edu/bioinformatics/

From the Biology department and in cooperation with Boston University. Emphasis on the ability to integrate knowledge from biological, computational, and mathematical disciplines.

Biocomp.unibo.it/lsbioinfo/

A two year, international master's programme in bioinformatics at the Universita di Bologna, Italy.

Cs.helsinki.fi/bioinformatiikka/mbi/programme.html

A two year Masters Degree Programme in Bioinformatics (MBI) offered by the University of Helsinki and Helsinki University of Technology, Finland.

Ornl.gov/sci/techresources/Human_Genome/education/education.shtml

A resource for introductory information on the Human Genome Project.

His.se/bioinformatics

A one-year, international master's programme in bioinformatics at the University of Skovde, Sweden.

Members.tripod.com/C.elegans/

Resources in biochemical, molecular, cellular, system, and organism biology, including over 25,000 indexed links, accumulated since 2000, from topic menus or from search interface.

Bioinformatics.org/faq/#contents

Summary of basics of bioinformatics for the intelligent newcomer.

Jiscmail.ac.uk/archives/bioinformatics.html

Forum featuring various aspects, events and developments in the bioinformatics field.

Biinoida.blogspot.com

Blog focusing on bioinformatics, biotechnology, pharma regulatory affairs, IPR and clinical trials.

Colorbasepair.com/bioinformatics_courses_tutorials.html

A list of on-line course materials and tutorials for bioinformatics and computational biology.

Geospiza.com/education/

Instructional materials for teaching bioinformatics. These include animated tutorials on topicssuch as BLAST, finding mutations in a protein, and graphing with MS-Excel.

Bioinformatics.fi

An international, two-year Master's programme jointly managed by the University of Tampere and the University of Turku, Finland.

Perlsource.net

Provides online courses in Perl programming for bioinformatic tools.