<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26617?offset=790 https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification Tue, 08 Nov 2022 03:39:11 -0600 https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification <![CDATA[Tools for RNA classification]]> barrnap - https://github.com/tseemann/barrnap

CPAT - https://github.com/liguowang/cpat, http://lilab.research.bcm.edu/ (web server)

CPC2 - https://github.com/gao-lab/CPC2_standalone, http://cpc2.gao-lab.org/ (web server)

Infernal - http://eddylab.org/infernal/, https://github.com/EddyRivasLab/infernal

NCBI RefSeq - https://www.ncbi.nlm.nih.gov/refseq/

Rfam - http://rfam.xfam.org/, https://docs.rfam.org/en/latest/index.html

SILVA - https://www.arb-silva.de/

RNAmmer - http://www.cbs.dtu.dk/services/RNAmmer/ (web server, standalone download link)

]]> Abhi https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis Tue, 07 Nov 2017 05:33:33 -0600 https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis <![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]>

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

https://github.com/bioinfo-ut/GenomeTester4/

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

http://clark.cs.ucr.edu/

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads Tue, 08 May 2018 04:27:22 -0500 https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads <![CDATA[HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads]]> HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs [Sirén et al. 2014], we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM). 

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/37915/dna-nucleotide-counter Fri, 12 Oct 2018 04:37:01 -0500 https://bioinformaticsonline.com/bookmarks/view/37915/dna-nucleotide-counter <![CDATA[DNA Nucleotide Counter]]> DNA Nucleotide Counter is delivered in a DNA Baser package together with other free molecular biology tools. Download the package and double click it. The programs inside the package will be extracted to the destination folder (specified by you). Go to the destination folder and double click the program you want to use.

It installs in any computer even if you don't have administrator rights!

Address of the bookmark: http://www.dnabaser.com/download/DNA-Counter/index.html

]]> Neel https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools Sat, 08 Aug 2020 08:25:21 -0500 https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools <![CDATA[ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.]]> How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

]]> Shruti Paniwala https://bioinformaticsonline.com/pages/view/43728/short-read-assembly-using-spades Mon, 31 Jan 2022 07:18:16 -0600 https://bioinformaticsonline.com/pages/view/43728/short-read-assembly-using-spades <![CDATA[Short-read assembly using Spades !]]> If we only had Illumina reads, we could also assemble these using the tool Spades.

You can try this here, or try it later on your own data.

Get data

We will use the same Illumina data as we used above:

  • illumina_R1.fastq.gz: the Illumina forward reads
  • illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina
  • -1 is input file of forward reads
  • -2 is input file of reverse reads
  • --careful minimizes mismatches and short indels
  • --cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
  • -o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta
]]> Abhimanyu Singh https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr Sat, 08 Jun 2024 15:59:46 -0500 https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr <![CDATA[Bioinformatic tools for pathogens informatics at CVR]]>

Novel sequencing and analytical approaches focused on studying viruses and virus-host interactions. Below you will find summaries and links to a number of bioinformatic tools that have been developed @ CVR.

The database-integrated genome-screening (DIGS) tool provides a framework for implementing automated in silico screening of sequence databases using BLAST in combination with a relational database (MySQL).

DisCVR is a Diagnostic tool for detecting known human viruses in clinical samples from Next-Generation Sequencing (NGS) data. The tool uses a simple and straightforward Graphical User Interface and is optimized on Windows OS without compromising speed and accuracy.

DiversiTools is a computational tool that is specifically tailored towards viral HTS data sets and the analysis of the underlying viral populations that they represent. It was initially developed in collaboration with a number of virologists interested in characterising the intra-host diversity of viral populations and studying their evolution across transmission chains at the micro-evolutionary scale.

GLUE is a flexible data-centric bioinformatics environment for virus sequence data, with a focus on virus evolution and genomic variation. GLUE has been applied to a range of viruses. A GLUE-based resource focused on Hepatitis C virus is HCV-GLUE.

Tanoti is a BLAST guided reference based short read aligner. It is developed for maximising alignment in highly variable next generation sequence data sets (Illumina).

ViCTree is a bioinformatic framework that automatically selects new candidate virus sequences from GenBank, generates multiple sequence alignments, calculates a maximum likelihood phylogeny and integrates the sequences into the existing phylogenetic trees. For more information click here.

Viral Host Predictor provides a fast and simple way to predict the hosts and vectors of RNA viruses from viral sequences.

GRACy is a bioinformatic tool designed for the analysis of Illumina data originated from Human cytomegalovirus samples. GRACy can be used to perform read quality filtering, genotyping, de novo assembly, variant detection, annotation and data submission to public database.

LoReTTA (Long Read Template Targeted Assembler) is a reference assisted de novo assembler specifically designed to deal with PacBio reads generated from viral genomes. 

BingleSeq is a R-package enables the user-friendly analysis of count tables obtained by both Bulk RNA-Seq and single-cell RNA-Seq protocols. The development of BingleSeq focused on providing a flexible and intuitive user experience.

]]> Abhi https://bioinformaticsonline.com/blog/view/33629/list-of-universities-offering-bachelor-master-or-phd-bioinformatics-degree-in-malaysia Thu, 22 Jun 2017 01:34:02 -0500 https://bioinformaticsonline.com/blog/view/33629/list-of-universities-offering-bachelor-master-or-phd-bioinformatics-degree-in-malaysia <![CDATA[List of universities offering Bachelor, Master or PhD bioinformatics degree in Malaysia]]> Bioinformatics is a newly emerging interdisciplinary research area, which may be defined as the ―interface between biological and computational sciences. Most of the Bioinformatics work that is done can be described as analyzing biological data, although a growing number of projects deal with the organization of biological information. The global Bioinformatics industry has grown at a double-digit growth rate in the past and is expected to follow the same pattern in the next four years. US remains the largest market in the world, but Asia-Pacific countries, particularly India and China, are witnessing the fastest growth and are anticipated to emerge as the dominating forces in future. The Comparison of Bioinformatics Industry between Malaysia, India and other countries are discussed in this http://ijbssnet.com/journals/Vol.%202_No._10;_June_2011/11.pdf paper.

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.

]]> sahabuddin https://bioinformaticsonline.com/opportunity/view/21685/uiar-short-term-trainingfinal-year-dissertation-project-in-life-sciencesbioinformaticsbiotech Mon, 16 Mar 2015 23:56:25 -0500 <![CDATA[UIAR Short-Term Training/Final Year Dissertation Project in Life Sciences/Bioinformatics/Biotech]]> Short-term training/Final year dissertation project

Candidates desirous of doing a short-term training / final year dissertation project for MSc (Life Sciences/Bioinformatics/Biotechnology or any science discipline) at UIAR Biophysics and Bioinformatics department may please drop an email atanju@iiar.res.in along with their resume.

Selected candidates will be further intimated. There will be a fees charged for doing the project at UIAR. The projects will be experimental or computational or involve both.

The training scope will be in the following areas but not limited to:

Bioinformatics analysis, Docking and Virtual screening, Molecular Dynamics simulation, Cloning, expression and purification of proteins, Biophysical and Biochemical characterisation of proteins, Crystallization and Structural Studies.

Advertisement: www.iiar.res.in/?q=node/450

]]> https://bioinformaticsonline.com/opportunity/view/24178/essentials-of-statistics-and-data-analysis-using-r Mon, 31 Aug 2015 01:32:12 -0500 <![CDATA[Essentials of Statistics and Data Analysis using R]]> Clinical Development Services Agency (CDSA) is an extramural unit of Translational Health Science and Technology Institute (THSTI), Department of Biotechnology, Ministry of Science & Technology, Government of India. CDSA has a national mandate of strengthening capacity and capability building in the area of Clinical development and Translational Research.

CDSA is pleased to announce a 4 days hands-on training program on “Essentials of Statistics and Data Analysis using R” at ICGEB, Aruna Asaf Ali Road, New Delhi on December 1 – 4, 2015. This will involve developing and enhancing skills to understand basic principles of statistics for summarizing data and use of appropriate statistical tests as well as providing an understanding of data analysis using R. Didactic lectures with practical sessions will be delivered by experienced faculties from AIIMS and Novartis. Live classroom with power point presentations, case studies, mock exercise, practical sessions on R, group work with time for discussion and Q&A sessions are added advantages of this workshop.

Please contact gayatrivishwakarma.cdsa@thsti.res.in or vineetabaloni.cdsa@thsti.res.in for program and registration details.

Please nominate personage or register yourself on or before November 6, 2015 along with the electronic transfer of registration fee.

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