CABOG is one of many software programs called genome assemblers. These programs exist to overcome the fundamental limitation of all sequencing machines, namely, that they read out very few DNA letters at a time. These programs reconstruct genomes that are billions of letters long from the hundreds of letters per read that modern sequencers provide. What these programs do is often described as a scaled up version of a family solving a jigsaw puzzle.

The CABOG software was the first to accomplish many scientific goals. It was the first to assemble the genome of a multicellular organism (Drosophila melanogaster, 2000). It was the first to assemble both parental haplotypes of one human genome (J. Craig Venter, 2007). It was the first to assemble environmental sequence from the oceans (Sargasso Sea in 2004 and Global Ocean Sampling in 2007). It was first to combine reads from first-generation Sanger sequencing machines and second-generation pyrosequencing machines (Marine microbes, 2006). Today, CABOG is one of the leading assembly programs for data sets that include paired end data from the Roche 454 line of sequencing machines.

Address of the bookmark: http://www.jcvi.org/cms/research/projects/cabog/overview/

http://gmod.org/wiki/Diya

Address of the bookmark: https://sourceforge.net/projects/diyg/

1. Must have basic understanding of molecular biology and Genomics.
2. Experience in application development or must have expertise in programming using either of Perl/Python.
3. Experience in statistical programming using R/Bioconductor/Matlab.
4. Strong concept in statistical and mathematical modelling.
5. Experience in designing and developing the bioinformatics pipeline.
6. Must have minimum 2+ years of hands on experience in NSG data analysis such as RNA-Seq,Exome-Seq ,Chip-Seq and downstream analysis.
7. Knowledge in WGS ,WES, Targeted re-sequencing,GWAS and population genomics will be preferred.
8. Must have experience working on opensource software/Framework and commercial software for NGS data analysis and reporting.
9. Should be aware of handling big data and guiding team members on multiple projects simultaneously.
10. Should have experience coordinating with different groups of clinical research scientist for various project requirements.
11. Ability to work as team as well as independently with minimal support.

More at http://www3.ocimumbio.com/

Next generation quantitative genetics in plants. Jiménez-Gómez, Frontiers in Plant Science 2:77, 2011 Full Text [equally relevant to animal and microbial systems]

Sense from sequence reads: methods for alignment and assembly. Flicek & Birney, Nat Methods 6(11 Suppl):S6-S12, 2009. Full Text

Library construction and experimental design

Statistical design and analysis of RNA sequencing data. Auer & Doerge, Genetics 185(2):405-16, 2010. PubMedCentral

Biases in Illumina transcriptome sequencing caused by random hexamer priming. Hansen et al., Nucleic Acids Res. 38(12): e131, 2010. PubMedCentral

Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Aird et al, Genome Biology 12:R18, 2011 Full Text

Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of GC-biased genomes. Kozarewa et al, Nature Methods 6(4):291-5, 2009 PubMedCentral

Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Rohland & Reich, Genome Research 22(5): 939–946. PubMedCentral

Data formats, data management, and alignment software tools

The Sequence Alignment/Map format and SAMtools. Li et al, Bioinformatics 25(16):2078-9, 2009 PubMedCentral

SAM format specification file

Efficient storage of high throughput sequencing data using reference-based compression. Fritz et al, Genome Res 21(5):734-40, 2011. Full Text

Compression of DNA sequence reads in FASTQ format. Deorowicz & Grabowski, Bioinformatics 27(6):860-2, 2011. PubMed

Fast and accurate short read alignment with Burrows-Wheeler transform. Li & Durbin, Bioinformatics 25(14):1754-60, 2009. PubMedCentral

Improving SNP discovery by base alignment quality. Li H, Bioinformatics 27(8):1157-8, 2011. PubMed

BEDTools: a flexible suite of utilities for comparing genomic features. Quinlan and Hall, Bioinformatics 26:841-842, 2010. Publisher Website

Data quality assessment, filtering, and correction

SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. Cox et al, BMC Bioinformatics 11:485, 2010. PubMedCentral

TileQC: a system for tile-based quality control of Solexa data. Dolan & Denver, BMC Bioinformatics 9:250, 2008 PubMedCentral [requires a reference sequence]

Quake: quality-aware detection and correction of sequencing errors. Kelley et al, Genome Biol 11(11):R116, 2010. PubMed

FastQC: a quality control tool for high-throughput sequence data. Home Page

FASTX-toolkit: FASTQ/A short-reads pre-processing tools Home Page

Reference-free validation of short read data. Schröder et al, PLoS One 5(9):e12681, 2010. PubMedCentral

Correction of sequencing errors in a mixed set of reads. Salmela, Bioinformatics 26(10):1284, 2010. Full Text [includes error correction of SOLiD reads in colorspace]

Repeat-aware modeling and correction of short read errors. Yang et al, BMC Bioinformatics 12(Supp1):S52, 2011 PubMedCentral [requires a reference sequence]

HiTEC: accurate error correction in high-throughput sequencing data. Ilie et al, Bioinformatics 27(3):295, 2011 Full Text

Error correction of high-throughput sequencing datasets with non-uniform coverage. Medvedev et al., Bioinformatics 27(13):i137-41, 2011. PubMedCentral

De novo assembly

Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Zerbino & Birney, Genome Res 18(5):821-9, 2008. u>PubMedCentral

Assembly of large genomes using second-generation sequencing. Schatz et al, Genome Res 20(9):1165-73, 2010. PubMedCentral

High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Gnerre et al, PNAS 108(4): 1513-18, 2011 PubMedCentral

Genome assembly has a major impact on gene content: a comparison of annotation in two Bos taurus assemblies. Florea  et al., PLoS One 6(6):e21400, 2011. PubMedCentral

Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Carver et al, Bioinformatics 28(4):464 - 469, 2012 PubMedCentral

Efficient de novo assembly of large genomes using compressed data structures. Simpson & Durbin, Genome Research 22:549-556, 2012 Full Text [Describes the String Graph Assembler (SGA), which assembled a human genome in less than 6 days using 54 Gb of RAM and a 123-processor compute cluster for calculation of an FM-index of the 1.2 billion reads]

Readjoiner: a fast and memory efficient string graph-based sequence assembler. Gonnella & Kurtz, BMC Bioinformatics 13: 82, 2012 PubMedCentral

Assemblathon 1: A competitive assessment of de novo short read assembly methods. Earl et al, Genome Research 21:2224-2241, 2011 Full Text

Chromatin immunoprecipation analysis: ChIP-seq

ChIP-seq: advantages and challenges of a maturing technology. Park, Nat Rev Genet. 10:669-80, 2009 PubMed

ChIP-seq and Beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Furey, Nat Rev Genet 13: 840–852, 2012 Publisher Web Site

MuMoD: a Bayesian approach to detect multiple modes of protein–DNA binding from genome-wide ChIP data. Narlikar, Nucleic Acids Res 41:21–32, 2013 PubMed

Transcriptome analysis

Assembly and comparison to genome

Full-length transcriptome assembly from RNA-Seq data without a reference genome. Grabherr et al, Nature Biotechnology 29:644 - 652, 2011. PubMed [The software is called Trinity, and is available on Sourceforge.]

Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Peng et al, Nature Biotechnology 30:253 - 260, 2012. PubMed [Several comments on this paper question whether the reported differences are in fact evidence of editing or are simply sequencing errors - the authors stand by their conclusions, but the controversy demonstrates the importance of robust data analysis methods.]

Optimization of de novo transcriptome assembly from next-generation sequencing data. Surget-Groba & Montoya-Burgos, Genome Res 20(10):1432-40, 2010. Full Text

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. Martin et al, BMC Genomics 11:663, 2010 Full Text

De novo assembly and analysis of RNA-seq data. Robertson et al, Nature Methods 7:909-912, 2010 Full Text [describes Trans-ABySS, a pipeline to use the ABySS parallel assembler for de novo transcriptome analysis]

Differential expression analysis

R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data. Mittal & McDonald, Nucleic Acids Res, 2012 Full Text

Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Mercer et al, Nature Biotechnology 30:99 - 104, 2012 Publisher Website

Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks. Trapnell et al, Nature Protocols 7:562 - 578, 2012 Publisher Website

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Łabaj et al, Bioinformatics 27:i383 - i391, 2011 Full Text

Improving RNA-Seq expression estimates by correcting for fragment bias. Roberts et al, Genome Biol 12:R22, 2011 PubMed Central

Cloud-scale RNA-sequencing differential expression analysis with Myrna. Langmead et al, Genome Biol 11:R83, 2010 Full Text

From RNA-seq reads to differential expression results. Oshlack et al, Genome Biol 11(12):220, 2010 Full Text

DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Wang et al., Bioinformatics. 26(1):136-8. 2010 PubMed

DEseq: Differential expression analysis for sequence count data. Anders and Huber, Genome Biology 11:R106, 2010 Full Text

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Robinson et al., Bioinformatics 26(1):139-40 2010 PubMedCentral

Two-stage Poisson model for testing RNA-seq data. Auer and Doerge, SAGMB 10(1), article 26 Full Text

Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. McCormick et al., Silence2(1):2, 2011 PubMedCentral

RNA-Seq gene expression estimation with read mapping uncertainty. Li et al, Bioinformatics 26:493-500, 2010 PubMedCentral [describes the RSEM software package]

Comparing genomes and assemblies; variant detection

Versatile and open software for comparing large genomes. Kurtz et al, Genome Biol (5(2):R12, 2004. PubMedCentral [describes the MUMmer software for full-genome alignment & comparisons]

Searching for SNPs with cloud computing. Langmead et al, Genome Biol 10(11):R134, 2009 Full Text

Calling SNPs without a reference sequence. Ratan et al, BMC Bioinformatics 11:130, 2010 PubMedCentral

Microindel detection in short-read sequence data. Krawitz et al, Bioinformatics 26(6):722-9, 2010. Full Text

vipR: variant identification in pooled DNA using R. Altmann et al., Bioinformatics 27: i77-i84, 2011. PubMedCentral

Geoseq: a tool for dissecting deep-sequencing datasets. Gurtowski et al, BMC Bioinformatics 11:506, 2010. PubMedCentral [Geoseq is a web service that allows searching deep sequencing datasets with a reference sequence of a gene of interest]

Detecting and annotating genetic variations using the HugeSeq pipeline. Lam et al, Nature Biotechnology 30:226 - 229, 2012 Publisher Website, Home Page

Genome-wide LORE1 retrotransposon mutagenesis and high-throughput insertion detection in Lotus japonicus. Urbański et al, Plant J 64:731-741, 2012. Publisher Website [This paper describes a 2-dimensional pooling strategy with barcoding to allow use of Illumina sequencing to screen for retrotransposon insertion mutations, and includes a software package called FSTpoolit for analysis of the resulting sequence reads.]

Genotyping by sequencing

Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Davey et al., Nat Rev Genet 12(7):499-510, 2011 PubMed [A review of methods available at the time]

A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. Elshire et al., PLoS One 6(5):e19379, 2011. Full Text

Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. Poland et al., PLoS One 7(2): e32253, 2012. Full Text

Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. Peterson et al, PLoS One 7(5):e37135, . 2012. Full Text

Imputation of unordered markers and the impact on genomic selection accuracy. Rutkowski et al, G3 3(3):427-39, 2013. Full Text

Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high-throughput, highly informative genotyping for molecular breeding of Eucalyptus. Sansaloni et al., BMC Proceedings 5(Suppl 7):P54, 2011 Full Text

High-throughput genotyping by whole-genome resequencing. Huang et al., Genome Res 19(6):1068-76, 2009. Full Text

Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Andolfatto et al. Genome Res 21(4):610-7, 2011. Full Text

Restriction-site Associated DNA (RAD) markers

Rapid SNP discovery and genetic mapping using sequenced RAD markers. Baird et al, PLoS One 3(10):e3376, 2008 Full Text

Linkage mapping and comparative genomics using next-generation RAD sequencing of a non-model organism. Baxter et al., PLoS One 6(4):e19315, 2011. Full Text

Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an outgroup for the teleost genome duplication. Amores et al, Genetics 188(4):799-808, 2011. PubMed

Construction and application for QTL analysis of a Restriction-site Associated DNA (RAD) linkage map in barley. Chutimanitsakun et al, BMC Genomics 4; 12:4, 2011. Full Text

RAD tag sequencing as a source of SNP markers in Cynara cardunculus L. Scaglione et al., BMC Genomics 13:3, 2012. Full Text

Paired-end RAD-seq for de novo assembly and marker design without available reference. Willing et al., Bioinformatics 27(16):2187-93, 2011. Publisher Website

Local de novo assembly of RAD paired-end contigs using short sequencing reads. Etter et al., PLOS ONE 6(4): e18561, 2011. Full Text

Stacks: building and genotyping loci de novo from short-read sequences. Catchen et al., G3: Genes, Genomes, Genetics, 1:171-182, 2011. Full Text, Home Page

Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads. Chong et al, Bioinformatics 28(21):2732-7, 2012. Publisher Website

UK RAD Sequencing Wiki page, with bibliography and RADTools software download Home Page

Workspace environments

Papers

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Goecks et al, Genome Biol 11(8):R86, 2010 PubMedCentral

Galaxy Cloudman: Delivering compute clusters. BMC Bioinformatics 11(Suppl. 12):S4, 2010 Full Text

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. McKenna et al, Genome Res 20(9):1297-303, 2010. PubMedCentral

A framework for variation discovery and genotyping using next-generation DNA sequencing data. DePristo et al., Nat Genet 43(5):491-8, 2011. PubMed

Online resources

The R statistical computing environment includes Bioconductor, a specialized set of tools for analysis of microarray and high-throughput sequencing data. Introductory materials from on-line or short workshops are widely available online; examples are Evomics2012 Bioconductor-tutorial.pdf, and Intro to Bioconductor. Materials from an advanced course on high-throughput genetic data analysis are at Seattle 2012 materials. Thomas Girke of UC-Riverside has written a very complete set of manuals describing the use of R and Bioconductor for analysis of genomic datasets, available at R and Bioconductor Manuals.
Manuals and contributed documentation for R are available at the R-project.org website, and video tutorials are also available on Youtube; those posted by Tutorlol are brief, clear, and to the point.
Materials from a series of mini-courses in R taught in 2010 at UCLA are available:

• Intro to programming and graphics
• Data manipulation and functions
• Graphics for exploratory data analysis
• Introductory statistics
• Linear regression

A Little Book of R for Bioinformatics is an on-line resource with information and exercises to provide practice in bioinformatics analysis of DNA sequences and other biological data in R.
Many books on specific topics in R programming are also available through Amazon or other vendors.

Cloud computing resources

The case for cloud computing in genome informatics. Lincoln Stein, Genome Biol. 11(5):207, 2010 Pubmed

Galaxy Cloudman: delivering cloud compute clusters. Afgan et al, BMC Bioinformatics 11(Suppl 12):S4, 2010 Full Text

CloudBioLinux is an open-source project that provides a bioinformatics Linux system for cloud computing, pre-configured with a variety of software tools installed and ready to use.

A tutorial on getting started with CloudBioLinux on the Amazon Web Services Elastic Compute Cloud (EC2)

“Deploying Galaxy on the Cloud” – slides from a presentation by Enis Afgan (Emory University) at the
 Bioinformatics Open Source Conference in Boston, July 2010

A screencast that provides a step-by-step guide to starting a Galaxy cluster in the EC2 environment

A webpage that has the same information in text form, and is the basis for the screencast

The iPlant Collaborative, an NSF-funded project to create computational resources for plant biology research, provides access to cloud computing resources through Atmosphere

SeqWare Query Engine: storing and searching sequence data in the cloud. O’Connor et al, BMC Bioinformatics 11(Suppl 12):S2, 2010 Full Text

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. Taylor, BMC Bioinformatics 11(Suppl 12):S1, 2010 Full Text

Links to Linux command-line tutorials and resources

Tutorials for AWK, a powerful tool for handling data tables

• A set of awk notes from Boston University
• Bruce Barnett's awk tutorial
• Greg Goebel's awk tutorial
• Executing an awk command from R to simplify data exploratory analysis, from Lex Nederbragt

Tutorials for bash shell scripting

• A tutorial at linuxconfig.org
• A Getting Started With Bash tutorial at hypexr.org
• Mendel Cooper's Advanced Bash Shell-Scripting Guide

Tutorials for sed, the command-line stream editor

• A tutorial at Rutgers
• Peteris Krumins claims to have the World's Best Introduction to Sed; take a look and judge for yourself.
• Bruce Barnett's sed tutorial.

Links to other useful sites

The SEQanswers online community has forums on several topics related to sequencing; the bioinformatics forum is the most active.

The SEQanswers Software Wiki is a list of software for analysis of sequencing data

Biostar is another online community for questions and answers on bioinformatics and computational genomics.

Information on file formats used by the University of California - Santa Cruz Genome Browser is on the FAQ list

A manual for the Integrated Genome Browser visualization tool is here

Course materials for a short course entitled Introduction to R and Bioconductor, held in Seattle in Dec 2010

Genomic Regions Enrichment of Annotations Tool - A web service to test for over-representation of specific ontology categories among genes near ChIP-seq peaks

Next-gen-seq software - a list of software packages, both commercial and open-source, related to analysis of deep sequencing datasets

Software from the Center for Bioinformatics and Computational Biology, University of Maryland - many useful programs, all open-source

PLAZA: a comparative genomics resource to study gene and genome evolution in plants; described by Proost et al, Plant Cell 21:3718, 2010 Full Text

The European Bioinformatics Institute provides tools ArrayExpressHTS and R-Cloud for analysis of transcriptome data

RASA offers faster, better and cost effective cutting edge technology solutions to chemical and life science research and industry. We provide our customers with A seamless model of wide expertise and comprehensive platforms. Our Value is to take our customers

Read more: https://edu.t-bio.info/amity-university-summer-bioinformatics-program-registrations-are-open/

Biostrings

The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. Documentation

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

Reference

Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014) Orione, a web-based framework for NGS analysis in microbiology. Bioinformatics [Epub ahead of print]. [article]

Address of the bookmark: http://orione.crs4.it/

a) M.Sc/or equivalent in biological sciences/related areas [Position Code: PA_JRF_PF_a]
b) B.E/B.Tech/ M.Sc in biotechnology/bioinformatics/computer science/Chemistry/Physics or MCA [Position Code: PA_JRF_PF_b]
c) M.Sc/or equivalent in wildlife sciences/ecology/environmental sciences or MBBS/BVSc/MVSc. [Position Code: PA_JRF_PF_c]

(Candidates with result awaited are NOT eligible to apply)

Upper Age limit 28years

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

2. Post Doctoral Fellow/Research Associate in multiple research areas [PDF_RA]

Ph.D. (submitted/awarded) in any branch of biological Sciences. Candidates with Ph.D. in other sciences are also encouraged to apply.

Experience in molecular biology, biochemistry, structural biology, cell biology, infectious disease, conservation genetics, veterinary science, reproductive biology, and molecular diagnostics is desired but not mandatory.

[Position Code: PDF_RA]

UpperAge limit 35years

Rs. 22000- 26000 (as sanctioned by the funding agency)

3. Post Doctoral Scientist Fellow [PDSF]

Ph.D in any of the following areas: bioinformatics, next generation sequencing, high throughput data analysis, proteomics, bio-statistics, computer science, information technology, computer hardware and networking/clustering, parallel processing.
[Position Code: PDSF]

Upper Age limit 40 years

Rs. 40000 consolidated (as sanctioned by the funding agency)

Download Application: Last date for apply online: 09th Oct 2014

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Apply online http://www.ccmb.res.in/positions/temp_notif/online_form.html

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