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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/11457?offset=560</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4090/computational-biology-in-the-21st-century-making-sense-out-of-massive-data</guid>
	<pubDate>Thu, 29 Aug 2013 08:32:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4090/computational-biology-in-the-21st-century-making-sense-out-of-massive-data</link>
	<title><![CDATA[Computational Biology in the 21st Century: Making Sense out of Massive Data]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/I99UiA_vaJQ" frameborder="0" allowfullscreen></iframe>Computational Biology in the 21st Century: Making Sense out of Massive Data    
    
Air date:  Wednesday, February 01, 2012, 3:00:00 PM
Category:  Wednesday Afternoon Lectures  
 
Description:  The last two decades have seen an exponential increase in genomic and biomedical data, which will soon outstrip advances in computing power to perform current methods of analysis. Extracting new science from these massive datasets will require not only faster computers; it will require smarter algorithms. We show how ideas from cutting-edge algorithms, including spectral graph theory and modern data structures, can be used to attack challenges in sequencing, medical genomics and biological networks. 

The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. 

Author:  Dr. Bonnie Berger  
Runtime:  00:58:06  
Permanent link:  http://videocast.nih.gov/launch.asp?17563]]></description>
	
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2334/binc-bioinformatics-national-certification-website-address</guid>
	<pubDate>Wed, 14 Aug 2013 09:40:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2334/binc-bioinformatics-national-certification-website-address</link>
	<title><![CDATA[BINC (BioInformatics National Certification) Website address]]></title>
	<description><![CDATA[<p><span>BINC (BioInformatics National Certification) is an initiative of Department of Biotechnology(DBT), Government Of India in coordination with Bioinformatics Center, University of Pune. The objective of the examination is to recognize trained manpower in the area of Bioinformatics. Currently, various Indian universities, Government and private institutions are involved in imparting courses in Bioinformatics in India.</span></p>
<p>Foreign nationals intending to have certification are eligible to appear for BINC examination.<br>Minimum qualification includes a degree from a recognized university/institute in the areas listed in FAQ.<br>Formal training in the area of Bioinformatics is not a prerequisite.<br>Note that the foreign students will only be certified by DBT and are not eligible for the cash award as well as junior research fellowship.</p><p>Address of the bookmark: <a href="http://binc.scisjnu.ernet.in/" rel="nofollow">http://binc.scisjnu.ernet.in/</a></p>]]></description>
	<dc:creator>Kamalakshi Mukherjee</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/7674/useful-publications-and-websites-for-deep-sequencing-data-analysis</guid>
	<pubDate>Sun, 29 Dec 2013 22:30:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/7674/useful-publications-and-websites-for-deep-sequencing-data-analysis</link>
	<title><![CDATA[Useful Publications and Websites for Deep Sequencing Data Analysis]]></title>
	<description><![CDATA[<h3>Global overview papers</h3><p>Next generation quantitative genetics in plants. Jim&eacute;nez-G&oacute;mez, Frontiers in Plant Science 2:77, 2011 <span style="text-decoration: underline;"><a href="http://www.frontiersin.org/Plant_Physiology/10.3389/fpls.2011.00077/full">Full Text</a> </span><em>[equally relevant to animal and microbial systems]</em></p><p>Sense from sequence reads: methods for alignment and assembly. Flicek &amp; Birney, Nat Methods 6(11 Suppl):S6-S12, 2009. <a href="http://www.nature.com/nmeth/journal/v6/n11s/full/nmeth.1376.html"><span style="text-decoration: underline;">Full Text</span></a></p><h3>Library construction and experimental design</h3><p>Statistical design and analysis of RNA sequencing data. Auer &amp; Doerge, Genetics 185(2):405-16, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881125"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Biases in Illumina transcriptome sequencing caused by random hexamer priming. Hansen et al., Nucleic Acids Res. 38(12): e131, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896536"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Aird et al, Genome Biology 12:R18, 2011 <a href="http://genomebiology.com/2011/12/2/R18"><span style="text-decoration: underline;">Full Text</span></a></p><p>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 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2664327/"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Rohland &amp; Reich, Genome Research 22(5): 939&ndash;946. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337438/"><span style="text-decoration: underline;">PubMedCentral</span></a></p><h3>Data formats, data management, and alignment software tools<span style="text-decoration: underline;"> </span></h3><p>The Sequence Alignment/Map format and SAMtools. Li et al, Bioinformatics 25(16):2078-9, 2009 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723002"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>SAM format specification <a href="http://samtools.sourceforge.net/SAM1.pdf"><span style="text-decoration: underline;">file</span></a></p><p>Efficient storage of high throughput sequencing data using reference-based compression. Fritz et al, Genome Res 21(5):734-40, 2011. <a href="http://genome.cshlp.org/content/21/5/734.long"><span style="text-decoration: underline;">Full Text</span></a></p><p>Compression of DNA sequence reads in FASTQ format. Deorowicz &amp; Grabowski, Bioinformatics 27(6):860-2, 2011. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21252073"><span style="text-decoration: underline;">PubMed</span></a></p><p>Fast and accurate short read alignment with Burrows-Wheeler transform. Li &amp; Durbin, Bioinformatics 25(14):1754-60, 2009. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705234"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Improving SNP discovery by base alignment quality. Li H, Bioinformatics 27(8):1157-8, 2011. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21320865"><span style="text-decoration: underline;">PubMed</span></a></p><p>BEDTools: a flexible suite of utilities for comparing genomic features. Quinlan and Hall, Bioinformatics 26:841-842, 2010. <a href="http://bioinformatics.oxfordjournals.org/content/26/6/841.full.pdf+html"><span style="text-decoration: underline;">Publisher Website</span></a></p><h3>Data quality assessment, filtering, and correction</h3><p>SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. Cox et al, BMC Bioinformatics 11:485, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2956736"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>TileQC: a system for tile-based quality control of Solexa data. Dolan &amp; Denver, BMC Bioinformatics 9:250, 2008 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2443380"><span style="text-decoration: underline;">PubMedCentral</span></a> <em>[requires a reference sequence]</em></p><p>Quake: quality-aware detection and correction of sequencing errors. Kelley et al, Genome Biol 11(11):R116, 2010. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21114842"> <span style="text-decoration: underline;">PubMed</span></a></p><p>FastQC: a quality control tool for high-throughput sequence data. <a href="http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/"><span style="text-decoration: underline;">Home Page</span></a></p><p>FASTX-toolkit: FASTQ/A short-reads pre-processing tools <a href="http://hannonlab.cshl.edu/fastx_toolkit/"><span style="text-decoration: underline;">Home Page</span></a></p><p>Reference-free validation of short read data. Schr&ouml;der et al, PLoS One 5(9):e12681, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943903"> <span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Correction of sequencing errors in a mixed set of reads. Salmela, Bioinformatics 26(10):1284, 2010. <a href="http://bioinformatics.oxfordjournals.org/content/26/10/1284.long"><span style="text-decoration: underline;">Full Text</span></a> <em>[includes error correction of SOLiD reads in colorspace]</em></p><p>Repeat-aware modeling and correction of short read errors. Yang et al, BMC Bioinformatics 12(Supp1):S52, 2011 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044310"> <span style="text-decoration: underline;">PubMedCentral</span></a> <em>[requires a reference sequence]</em></p><p>HiTEC: accurate error correction in high-throughput sequencing data. Ilie et al, Bioinformatics 27(3):295, 2011 <a href="http://bioinformatics.oxfordjournals.org/content/27/3/295.long"><span style="text-decoration: underline;">Full Text</span></a></p><p>Error correction of high-throughput sequencing datasets with non-uniform coverage. Medvedev et al., Bioinformatics 27(13):i137-41, 2011. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117386"><span style="text-decoration: underline;">PubMedCentral</span></a></p><h3>De novo assembly<span style="text-decoration: underline;"> </span></h3><p>Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Zerbino &amp; Birney, Genome Res 18(5):821-9, 2008. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2336801">u&gt;PubMedCentral</a></p><p>Assembly of large genomes using second-generation sequencing. Schatz et al, Genome Res 20(9):1165-73, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928494"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Gnerre et al, PNAS 108(4): 1513-18, 2011 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3029755"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Genome assembly has a major impact on gene content: a comparison of annotation in two <em>Bos taurus </em> assemblies. Florea&nbsp; et al., PLoS One 6(6):e21400, 2011. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3120881/"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Carver et al, Bioinformatics 28(4):464 - 469, 2012 <span style="text-decoration: underline;"><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278759/">PubMedCentral</a></span></p><p>Efficient de novo assembly of large genomes using compressed data structures. Simpson &amp; Durbin, Genome Research 22:549-556, 2012 <span style="text-decoration: underline;"><a href="http://genome.cshlp.org/content/22/3/549.full">Full Text</a></span> <em>[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]</em></p><p>Readjoiner: a fast and memory efficient string graph-based sequence assembler. Gonnella &amp; Kurtz, BMC Bioinformatics 13: 82, 2012 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3507659"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Assemblathon 1: A competitive assessment of de novo short read assembly methods. Earl et al, Genome Research 21:2224-2241, 2011 <span style="text-decoration: underline;"><a href="http://genome.cshlp.org/content/early/2011/09/16/gr.126599.111.full.pdf+html">Full Text</a></span></p><h3>Chromatin immunoprecipation analysis: ChIP-seq</h3><p>ChIP-seq: advantages and challenges of a maturing technology. Park, Nat Rev Genet. 10:669-80, 2009 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191340/"><span style="text-decoration: underline;">PubMed</span></a></p><p>ChIP-seq and Beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Furey, Nat Rev Genet 13: 840&ndash;852, 2012 <a href="http://www.nature.com/nrg/journal/v13/n12/full/nrg3306.html"> <span style="text-decoration: underline;">Publisher Web Site</span></a></p><p>MuMoD: a Bayesian approach to detect multiple modes of protein&ndash;DNA binding from genome-wide ChIP data. Narlikar, Nucleic Acids Res 41:21&ndash;32, 2013 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592440/"><span style="text-decoration: underline;">PubMed</span></a></p><h3>Transcriptome analysis</h3><h3>Assembly and comparison to genome</h3><p>Full-length transcriptome assembly from RNA-Seq data without a reference genome. Grabherr et al, Nature Biotechnology 29:644 - 652, 2011. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21572440"><span style="text-decoration: underline;">PubMed</span></a> <em>[The software is called <a href="http://trinityrnaseq.sourceforge.net/"><span style="text-decoration: underline;">Trinity</span></a>, and is available on Sourceforge.]</em></p><p>Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Peng et al, Nature Biotechnology 30:253 - 260, 2012. <span style="text-decoration: underline;"><a href="http://www.ncbi.nlm.nih.gov/pubmed/22327324">PubMed</a></span> <em>[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.] </em></p><p>Optimization of de novo transcriptome assembly from next-generation sequencing data. Surget-Groba &amp; Montoya-Burgos, Genome Res 20(10):1432-40, 2010. <a href="http://genome.cshlp.org/content/20/10/1432.long"><span style="text-decoration: underline;">Full Text</span></a></p><p>Rnnotator: an automated <em>de novo</em> transcriptome assembly pipeline from stranded RNA-Seq reads. Martin et al, BMC Genomics 11:663, 2010 <a href="http://www.biomedcentral.com/1471-2164/11/663"><span style="text-decoration: underline;">Full Text</span></a></p><p><em>De novo</em> assembly and analysis of RNA-seq data. Robertson et al, Nature Methods 7:909-912, 2010 <a href="http://www.nature.com/nmeth/journal/v7/n11/full/nmeth.1517.html"><span style="text-decoration: underline;">Full Text</span></a> <em>[describes Trans-ABySS, a pipeline to use the ABySS parallel assembler for de novo transcriptome analysis]</em></p><h3>Differential expression analysis</h3><p>R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data. Mittal &amp; McDonald, Nucleic Acids Res, 2012 <span style="text-decoration: underline;"><a href="http://nar.oxfordjournals.org/content/early/2012/01/28/nar.gks047.long">Full Text</a></span></p><p>Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Mercer et al, Nature Biotechnology 30:99 - 104, 2012 <span style="text-decoration: underline;"><a href="http://www.nature.com/nbt/journal/v30/n1/full/nbt.2024.html"> Publisher Website</a></span></p><p>Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks. Trapnell et al, Nature Protocols 7:562 - 578, 2012 <span style="text-decoration: underline;"><a href="http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html"> Publisher Website</a></span></p><p>Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Łabaj et al, Bioinformatics 27:i383 - i391, 2011 <span style="text-decoration: underline;"><a href="http://bioinformatics.oxfordjournals.org/content/27/13/i383.full.pdf+html"> Full Text</a></span></p><p>Improving RNA-Seq expression estimates by correcting for fragment bias. Roberts et al, Genome Biol 12:R22, 2011 <span style="text-decoration: underline;"><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3129672/">PubMed Central</a></span></p><p>Cloud-scale RNA-sequencing differential expression analysis with Myrna. Langmead et al, Genome Biol 11:R83, 2010 <a href="http://genomebiology.com/2010/11/8/R83"><span style="text-decoration: underline;">Full Text</span></a></p><p>From RNA-seq reads to differential expression results. Oshlack et al, Genome Biol 11(12):220, 2010 <a href="http://genomebiology.com/content/11/12/220"><span style="text-decoration: underline;">Full Text</span></a></p><p>DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Wang et al., Bioinformatics. 26(1):136-8. 2010 <a href="http://www.ncbi.nlm.nih.gov/pubmed/19855105"><span style="text-decoration: underline;"> PubMed</span></a></p><p>DEseq: Differential expression analysis for sequence count data. Anders and Huber, Genome Biology 11:R106, 2010 <a href="http://genomebiology.com/2010/11/10/R106"><span style="text-decoration: underline;">Full Text</span></a></p><p>edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Robinson et al., Bioinformatics 26(1):139-40 2010 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818"> <span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Two-stage Poisson model for testing RNA-seq data. Auer and Doerge, SAGMB 10(1), article 26 <a href="http://www.bepress.com/sagmb/vol10/iss1/art26/"><span style="text-decoration: underline;">Full Text</span></a></p><p>Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. McCormick et al., Silence2(1):2, 2011 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3055805"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>RNA-Seq gene expression estimation with read mapping uncertainty. Li et al, Bioinformatics 26:493-500, 2010 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2820677">PubMedCentral</a> <em>[describes the RSEM software package]</em></p><h3>Comparing genomes and assemblies; variant detection<span style="text-decoration: underline;"> </span></h3><p>Versatile and open software for comparing large genomes. Kurtz et al, Genome Biol (5(2):R12, 2004. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC395750"><span style="text-decoration: underline;">PubMedCentral</span></a> <em>[describes the MUMmer software for full-genome alignment &amp; comparisons]</em></p><p>Searching for SNPs with cloud computing. Langmead et al, Genome Biol 10(11):R134, 2009 <a href="http://genomebiology.com/content/10/11/R134"><span style="text-decoration: underline;">Full Text</span></a></p><p>Calling SNPs without a reference sequence. Ratan et al, BMC Bioinformatics 11:130, 2010 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851604"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Microindel detection in short-read sequence data. Krawitz et al, Bioinformatics 26(6):722-9, 2010. <a href="http://bioinformatics.oxfordjournals.org/content/26/6/722.long"><span style="text-decoration: underline;">Full Text</span></a></p><p>vipR: variant identification in pooled DNA using R. Altmann et al., Bioinformatics 27: i77-i84, 2011. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117388"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Geoseq: a tool for dissecting deep-sequencing datasets. Gurtowski et al, BMC Bioinformatics 11:506, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2972303/"><span style="text-decoration: underline;">PubMedCentral</span></a> <em>[Geoseq is a web service that allows searching deep sequencing datasets with a reference sequence of a gene of interest]</em></p><p>Detecting and annotating genetic variations using the HugeSeq pipeline. Lam et al, Nature Biotechnology 30:226 - 229, 2012 <span style="text-decoration: underline;"><a href="http://www.nature.com/nbt/journal/v30/n3/full/nbt.2134.html">Publisher Website</a></span>, <span style="text-decoration: underline;"><a href="http://hugeseq.snyderlab.org/">Home Page</a></span></p><p>Genome-wide LORE1 retrotransposon mutagenesis and high-throughput insertion detection in <em>Lotus japonicus</em>. Urbański et al, Plant J 64:731-741, 2012. <span style="text-decoration: underline;"><a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1365-313X.2011.04827.x/abstract">Publisher Website</a></span> <em>[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.]</em></p><h3>Genotyping by sequencing</h3><p>Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Davey et al., Nat Rev Genet 12(7):499-510, 2011 <a href="http://www.ncbi.nlm.nih.gov/pubmed/21681211"><span style="text-decoration: underline;">PubMed</span></a> <em>[A review of methods available at the time]</em></p><p>A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. Elshire et al., PLoS One 6(5):e19379, 2011. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3087801"><span style="text-decoration: underline;">Full Text</span></a></p><p>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. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289635/"><span style="text-decoration: underline;">Full Text</span></a></p><p>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. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3365034/"><span style="text-decoration: underline;">Full Text</span></a></p><p>Imputation of unordered markers and the impact on genomic selection accuracy. Rutkowski et al, G3 3(3):427-39, 2013. <a href="http://www.g3journal.org/content/3/3/427.long"><span style="text-decoration: underline;">Full Text</span></a></p><p>Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high-throughput, highly informative genotyping for molecular breeding of <em>Eucalyptus</em>. Sansaloni et al., BMC Proceedings 5(Suppl 7):P54, 2011 <span style="text-decoration: underline;"><a href="http://www.biomedcentral.com/1753-6561/5/S7/P54">Full Text</a></span></p><p>High-throughput genotyping by whole-genome resequencing. Huang et al., Genome Res 19(6):1068-76, 2009. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694477"><span style="text-decoration: underline;">Full Text</span></a></p><p>Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Andolfatto et al. Genome Res 21(4):610-7, 2011. <a href="http://genome.cshlp.org/content/21/4/610.long"><span style="text-decoration: underline;">Full Text</span></a></p><h3>Restriction-site Associated DNA (RAD) markers</h3><p>Rapid SNP discovery and genetic mapping using sequenced RAD markers. Baird et al, PLoS One 3(10):e3376, 2008 <span style="text-decoration: underline;"><a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0003376">Full Text</a></span></p><p>Linkage mapping and comparative genomics using next-generation RAD sequencing of a non-model organism. Baxter et al., PLoS One 6(4):e19315, 2011. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3082572"><span style="text-decoration: underline;">Full Text</span></a></p><p>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. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21828280"><span style="text-decoration: underline;"> PubMed</span></a></p><p>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. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3023751"><span style="text-decoration: underline;">Full Text</span></a></p><p>RAD tag sequencing as a source of SNP markers in <em>Cynara cardunculus </em>L. Scaglione et al., BMC Genomics 13:3, 2012. <span style="text-decoration: underline;"><a href="http://www.biomedcentral.com/1471-2164/13/3">Full Text</a></span></p><p>Paired-end RAD-seq for de novo assembly and marker design without available reference. Willing et al., Bioinformatics 27(16):2187-93, 2011. <a href="http://bioinformatics.oxfordjournals.org/content/27/16/2187.long"><span style="text-decoration: underline;">Publisher Website</span></a></p><p>Local de novo assembly of RAD paired-end contigs using short sequencing reads. Etter et al., PLOS ONE 6(4): e18561, 2011. <a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0018561"><span style="text-decoration: underline;">Full Text</span></a></p><p>Stacks: building and genotyping loci de novo from short-read sequences. Catchen et al., G3: Genes, Genomes, Genetics, 1:171-182, 2011. <span style="text-decoration: underline;"> Full Text</span>, <a href="http://creskolab.uoregon.edu/stacks/"><span style="text-decoration: underline;">Home Page</span></a></p><p>Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads. Chong et al, Bioinformatics 28(21):2732-7, 2012. <a href="http://bioinformatics.oxfordjournals.org/content/28/21/2732.long"> <span style="text-decoration: underline;">Publisher Website</span></a></p><p>UK RAD Sequencing Wiki page, with bibliography and RADTools software download <a href="https://www.wiki.ed.ac.uk/display/RADSequencing/Home"><span style="text-decoration: underline;">Home Page</span></a></p><h3>Workspace environments</h3><p><span style="text-decoration: underline;">Papers</span></p><p>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 <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945788"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>Galaxy Cloudman: Delivering compute clusters. BMC Bioinformatics 11(Suppl. 12):S4, 2010 <a href="http://www.biomedcentral.com/content/pdf/1471-2105-11-S12-S4.pdf"><span style="text-decoration: underline;">Full Text</span></a></p><p><a href="http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit"><span style="text-decoration: underline;">The Genome Analysis Toolkit</span></a>: a MapReduce framework for analyzing next-generation DNA sequencing data. McKenna et al, Genome Res 20(9):1297-303, 2010. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928508"><span style="text-decoration: underline;">PubMedCentral</span></a></p><p>A framework for variation discovery and genotyping using next-generation DNA sequencing data. DePristo et al., Nat Genet 43(5):491-8, 2011. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21478889"><span style="text-decoration: underline;"> PubMed</span></a></p><p><span style="text-decoration: underline;">Online resources</span></p><p>The <a href="http://cran.r-project.org/"><span style="text-decoration: underline;">R statistical computing</span></a> environment includes<a href="http://www.bioconductor.org/"><span style="text-decoration: underline;"> Bioconductor</span></a>, 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 <span style="text-decoration: underline;"><a href="http://bioconductor.org/help/course-materials/2012/Evomics2012/Bioconductor-tutorial.pdf">Evomics2012 Bioconductor-tutorial.pdf</a></span>, and <a href="http://bcb.dfci.harvard.edu/%7Eaedin/courses/Bioconductor/"><span style="text-decoration: underline;">Intro to Bioconductor</span></a>. Materials from an advanced course on high-throughput genetic data analysis are at <span style="text-decoration: underline;"><a href="http://bioconductor.org/help/course-materials/2012/SeattleFeb2012/">Seattle 2012 materials</a></span>. 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 <a href="http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual">R and Bioconductor Manuals</a>. <br /> <a href="http://cran.r-project.org/manuals.html"><span style="text-decoration: underline;">Manuals</span></a> and contributed <a href="http://cran.r-project.org/other-docs.html"><span style="text-decoration: underline;">documentation</span></a> 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. <br /> Materials from a series of mini-courses in R taught in 2010 at UCLA are available:</p><ul>
<li><a href="http://scc.stat.ucla.edu/page_attachments/0000/0141/10S-basicR.pdf">Intro to programming and graphics</a></li>
<li><a href="http://scc.stat.ucla.edu/page_attachments/0000/0143/S10_RProgII.pdf">Data manipulation and functions</a></li>
<li><a href="http://scc.stat.ucla.edu/page_attachments/0000/0185/Graphics_course.pdf">Graphics for exploratory data analysis</a></li>
<li><a href="http://scc.stat.ucla.edu/page_attachments/0000/0147/20100503_IntroStats.pdf">Introductory statistics</a></li>
<li><a href="http://scc.stat.ucla.edu/page_attachments/0000/0188/reg_R_1_09S_slides.pdf">Linear regression</a></li>
</ul><p><a href="http://a-little-book-of-r-for-bioinformatics.readthedocs.org/en/latest/"> <span style="text-decoration: underline;">A Little Book of R for Bioinformatics</span></a> is an on-line resource with information and exercises to provide practice in bioinformatics analysis of DNA sequences and other biological data in R. <br /> Many books on specific topics in R programming are also available through Amazon or other vendors.</p><h3>Cloud computing resources</h3><p>The case for cloud computing in genome informatics. Lincoln Stein, Genome Biol. 11(5):207, 2010 <a href="http://www.ncbi.nlm.nih.gov/pubmed/20441614"><span style="text-decoration: underline;">Pubmed</span></a></p><p>Galaxy Cloudman: delivering cloud compute clusters. Afgan et al, BMC Bioinformatics <span style="text-decoration: underline;">11</span>(Suppl 12):S4, 2010 <a href="http://www.biomedcentral.com/1471-2105/11/S12/S4"><span style="text-decoration: underline;">Full Text</span></a></p><p><a href="http://cloudbiolinux.com/">CloudBioLinux</a> 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.</p><p>A <a href="https://github.com/chapmanb/cloudbiolinux/blob/master/doc/intro/gettingStarted_CloudBioLinux.pdf?raw=true"><span style="text-decoration: underline;">tutorial</span></a> on getting started with CloudBioLinux on the Amazon Web Services Elastic Compute Cloud (EC2)</p><p><a href="http://userwww.service.emory.edu/%7Eeafgan/content/ppt/EnisAfgan_BOSC_2010.pdf"><span style="text-decoration: underline;">Deploying Galaxy on the Cloud</span></a>  slides from a presentation by Enis Afgan (Emory University) at the <br /> &nbsp;Bioinformatics Open Source Conference in Boston, July 2010</p><p>A <a href="http://screencast.g2.bx.psu.edu/cloud/"><span style="text-decoration: underline;"> screencast</span></a> that provides a step-by-step guide to starting a Galaxy cluster in the EC2 environment</p><p>A <a href="https://bitbucket.org/galaxy/galaxy-central/wiki/cloud"><span style="text-decoration: underline;">webpage</span></a> that has the same information in text form, and is the basis for the screencast</p><p>The iPlant Collaborative, an NSF-funded project to create computational resources for plant biology research, provides access to cloud computing resources through <span style="text-decoration: underline;"><a href="http://www.iplantcollaborative.org/discover/atmosphere">Atmosphere</a></span></p><p>SeqWare Query Engine: storing and searching sequence data in the cloud. OConnor et al, BMC Bioinformatics <strong>11</strong>(Suppl 12)<strong>:</strong>S2, 2010 <a href="http://www.biomedcentral.com/1471-2105/11/S12/S2"><span style="text-decoration: underline;">Full Text</span></a></p><p>An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. Taylor, BMC Bioinformatics <strong>11</strong>(Suppl 12)<strong>:</strong>S1, 2010 <a href="http://www.biomedcentral.com/1471-2105/11/S12/S1"><span style="text-decoration: underline;">Full Text</span></a></p><h3>Links to Linux command-line tutorials and resources</h3><p>Tutorials for AWK, a powerful tool for handling data tables</p><ul>
<li>A set of <a href="http://people.bu.edu/scottm/AWK.NOTES"><span style="text-decoration: underline;">awk notes</span></a> from Boston University</li>
<li>Bruce Barnett's <a href="http://www.grymoire.com/Unix/Awk.html"><span style="text-decoration: underline;">awk tutorial</span></a></li>
<li>Greg Goebel's <a href="http://www.vectorsite.net/tsawk.html"><span style="text-decoration: underline;">awk tutorial</span></a></li>
<li><a href="http://teaching.software-carpentry.org/2013/01/16/1433/"><span style="text-decoration: underline;">Executing an awk command from R</span></a> to simplify data exploratory analysis, from Lex Nederbragt</li>
</ul><p>Tutorials for bash shell scripting</p><ul>
<li>A <a href="http://www.linuxconfig.org/bash-scripting-tutorial"><span style="text-decoration: underline;">tutorial</span></a> at linuxconfig.org</li>
<li>A <a href="http://www.hypexr.org/bash_tutorial.php"><span style="text-decoration: underline;">Getting Started With Bash</span></a> tutorial at hypexr.org</li>
<li>Mendel Cooper's <a href="http://tldp.org/LDP/abs/html/"><span style="text-decoration: underline;">Advanced Bash Shell-Scripting Guide</span></a></li>
</ul><p>Tutorials for sed, the command-line stream editor</p><ul>
<li>A <a href="http://www.panix.com/%7Eelflord/unix/sed.html"><span style="text-decoration: underline;">tutorial</span></a> at Rutgers</li>
<li>Peteris Krumins claims to have the <a href="http://www.catonmat.net/blog/worlds-best-introduction-to-sed/"><span style="text-decoration: underline;"> World's Best Introduction to Sed</span></a>; take a look and judge for yourself.</li>
<li>Bruce Barnett's <a href="http://www.grymoire.com/Unix/Sed.html"><span style="text-decoration: underline;">sed tutorial</span></a>.</li>
</ul><h3>Links to other useful sites</h3><p>The<a href="http://seqanswers.com/"><span style="text-decoration: underline;"> SEQanswers</span></a> online community has forums on several topics related to sequencing; the bioinformatics forum is the most active.</p><p>The SEQanswers <span style="text-decoration: underline;"><a href="http://seqanswers.com/wiki/Software">Software Wiki</a></span> is a list of software for analysis of sequencing data</p><p><a href="http://biostar.stackexchange.com/">Biostar</a> is another online community for questions and answers on bioinformatics and computational genomics.</p><p>Information on file formats used by the University of California - Santa Cruz Genome Browser is on the <a href="http://genome.ucsc.edu/FAQ/FAQformat"><span style="text-decoration: underline;"> FAQ list</span></a></p><p>A manual for the Integrated Genome Browser visualization tool is <a href="http://wiki.transvar.org/confluence/display/igbman/Home"><span style="text-decoration: underline;">here</span></a></p><p>Course materials for a short course entitled <a href="http://bioconductor.org/help/course-materials/2010/SeattleIntro/"><span style="text-decoration: underline;">Introduction to R and Bioconductor</span></a>, held in Seattle in Dec 2010</p><p><a href="http://great.stanford.edu/"><span style="text-decoration: underline;">Genomic Regions Enrichment of Annotations Tool</span></a> - A web service to test for over-representation of specific ontology categories among genes near ChIP-seq peaks</p><p><a href="http://www.animalgenome.org/bioinfo/resources/nextgensoft.html"><span style="text-decoration: underline;">Next-gen-seq software</span></a> - a list of software packages, both commercial and open-source, related to analysis of deep sequencing datasets</p><p><a href="http://www.cbcb.umd.edu/software/"><span style="text-decoration: underline;">Software</span></a> from the Center for Bioinformatics and Computational Biology, University of Maryland - many useful programs, all open-source</p><p><a href="http://bioinformatics.psb.ugent.be/plaza/"><span style="text-decoration: underline;"> PLAZA</span></a>: a comparative genomics resource to study gene and genome evolution in plants; described by Proost et al, Plant Cell 21:3718, 2010 <a href="http://www.plantcell.org/content/21/12/3718.full"><span style="text-decoration: underline;">Full Text</span></a></p><p>The European Bioinformatics Institute provides tools <a href="http://www.ebi.ac.uk/Tools/rcloud/"><span style="text-decoration: underline;">ArrayExpressHTS</span><span style="text-decoration: underline;"> and R-Cloud</span></a> for analysis of transcriptome data</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/44741/bioinformatician-in-pipeline-development</guid>
  <pubDate>Tue, 17 Dec 2024 23:43:54 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatician in pipeline development]]></title>
  <description><![CDATA[
<p>Are you interested in working with pipeline development in bioinformatics, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a position as Bioinformatician in pipeline development at Uppsala University.</p>

<p>National Bioinformatics Infrastructure Sweden (NBIS) (nbis.se) plays an important role in advancing life science research in Sweden by providing expert support and developing cutting-edge bioinformatics infrastructure. Operating as a truly national initiative, NBIS employs more than 120 bioinformaticians, system developers, and data stewards across multiple locations in Sweden. It serves as the bioinformatics platform at SciLifeLab, a national resource that facilitates research in molecular biosciences by offering access to state-of-the-art technologies and technical expertise. With strong ties to data-producing facilities and ongoing collaborations with leading research groups, NBIS is ideally positioned to support world-class bioinformatics analyses. Furthermore, NBIS is the Swedish node in ELIXIR, the European infrastructure for biological information.</p>

<p>NBIS is seeking an experienced bioinformatician to support both Swedish and international projects. As part of our dynamic team, you will work closely with researchers to process large-scale biological data and contribute to advancing our data analysis infrastructure. Strong problem-solving skills, attention to detail, and the ability to troubleshoot complex bioinformatics pipelines are essential for success in this role. Flexibility and a willingness to learn are also important, as NBIS continually adapts to meet the evolving needs of the Swedish research community.</p>

<p>More at https://www.uu.se/en/about-uu/join-us/jobs-and-vacancies/job-details?query=778701</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/2742/baumbach-lab</guid>
  <pubDate>Wed, 21 Aug 2013 10:56:35 -0500</pubDate>
  <link></link>
  <title><![CDATA[Baumbach Lab]]></title>
  <description><![CDATA[
<p>The Computational Biology research group was established in October 2012 at the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark (SDU). It emerged from the Computational Systems Biology group, founded in March 2010 at the Max Planck Institute for Informatics (MPII) and the Cluster of Excellence for Multimodel Computing and Interaction (MMCI) at Saarland University, Saarbrücken, Germany.<br />​<br />The group is headed by Prof. Dr. Jan Baumbach and currently hosts nine PhD students and one postdoctoral fellow at both, IMADA/SDU and MMCI/MPII.</p>

<p>More at &gt;&gt; http://www.baumbachlab.net/</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/2261/best-book-titles-for-learning-bionformatics</guid>
	<pubDate>Tue, 13 Aug 2013 17:31:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/2261/best-book-titles-for-learning-bionformatics</link>
	<title><![CDATA[Best book Titles for Learning Bionformatics]]></title>
	<description><![CDATA[<p>Nothing can add to our intellect more than reading a book. &nbsp;In books, we can experience new things that we would not normally be able to experience. It is proved that books can change our lives and other people&rsquo;s lives. Reading can make us more intelligent, updated, imaginative. Without reading we wouldn&rsquo;t know anything that we know today. There are several book, online and offile to read and I can't mentioned all of them here in the list. Therefore, I mentioned some bioinformatics and its related books in subgroups. Hope you will like the list.&nbsp;</p><p>Sequence Analysis and General Bioinformatics</p><ul>
<li>BLAST, Ian Korf, Mark Yandell, Joseph Bedell, 2003, O'Reilly</li>
<li>Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases, Scott Markel, Darryl Leon, 2003, O'Reilly</li>
<li>Bioinformatics for Geneticists, Michael Barnes, Ian C Gray (Editors), 2003, John Wiley &amp; Sons</li>
<li>Bioinformatics for Dummies, Jean-Michel Claverie, Cedric Notredame, 2003, John Wiley &amp; Sons</li>
<li>Mathematics of Genome Analysis, Jerome K. Percus, 2002, Cambridge Univ Press</li>
<li>Bioinformatics Computing, Bryan P. Bergeron, 2002, Prentice Hall</li>
<li>Evolutionary Computation in Bioinformatics, Gary B. Fogel, David W. Corne (Editors), 2002, Morgan Kaufmann</li>
<li>Introduction to Bioinformatics, Arthur M. Lesk, 2002, Oxford University Press</li>
<li>Instant Notes in Bioinformatics, D.R. Westhead, J. H. Parish, R.M. Twyman, 2002, Bios Scientific Pub</li>
<li>Fundamental Concepts of Bioinformatics, Dan E. Krane, Michael L. Raymer, Michaeel L. Raymer, Elaine Nicpon Marieb, 2002, Benjamin/Cummings</li>
<li>Essentials of Genomics and Bioinformatics, C. W. Sensen (Editor), 2002, John Wiley &amp; Sons</li>
<li>Current Topics in Computational Molecular Biology (Computational Molecular Biology), Tao Jiang, Ying Xu, Michael Zhang (Editors), 2002, MIT Press</li>
<li>Hidden Markov Models for Bioinformatics, Timo Koski, Timo Koskinen, 2001, Kluwer Academic Publishers</li>
<li>Bioinformatics: From Genomes to Drugs, Thomas Lengauer (Editor), 2001, John Wiley &amp; Sons</li>
<li>Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health), Warren Ewens, Gregory Grant, 2001, Springer Verlag</li>
<li>Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition, Andreas D. Baxevanis, B. F. Francis Ouellette, 2001, Wiley-Interscience</li>
<li>Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning), Pierre Baldi, Soren Brunak, Sren Brunak, 2001, MIT Press</li>
<li>Introduction to Bioinformatics, T eresa Attwood, David Parry-Smith, 2001, Prentice Hall</li>
<li>Bioinformatics: A Primer, Charles Staben, 2001, Jones &amp; Bartlett Pub</li>
<li>Data Analysis and Classification for Bioinformatics, Arun Jagota, 2000, AKJ Academics</li>
<li>Bioinformatics: Sequence and Genome Analysis, David W. Mount, 2001, Cold Spring Harbor Laboratory Press</li>
<li>Bioinformatics: A Biologist's Guide to Biocomputing and the Internet, Stuart M. Brown, 2000, Eaton Pub Co</li>
<li>Bioinformatics: Sequence, Structure and Databanks: A Practical Approach (The Practical Approach Series, 236), Des Higgins (Editor), Willie Taylor (Editor), 2000, Oxford Univ Press</li>
<li>Neural Networks and Genome Informatics, Cathy H. Wu, Jerry W. McLarty, 2000, Elsevier Science</li>
<li>Computational Molecular Biology: An Introduction (Wiley Series in Mathematical and Computational Biology), Peter Clote and Rolf Backofen, 2000, John Wiley &amp; Sons</li>
<li>Computational Molecular Biology: An Algorithmic Approach, Pavel A. Pevzner, 2000, MIT Press</li>
<li>Post-Genome Informatics, Minoru Kanehisa, 2000, Oxford Univ Press</li>
<li>Mathematical and Computational Biology: Computational Morphogenesis, Hierarchical Complexity, and Digital Evolution, Chrystopher L. Nehaniv, 1999, American Mathematical Society</li>
<li>Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications, Jason T. L. Wang, Bruce A. Shapiro, Dennis Elliott Shasha (Editors), 1999, Oxford Univ Press</li>
<li>Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, David Sankoff and Joseph Kruskal (Editors), 1999, Cambridge University Press</li>
<li>Bioinformatics Basics: Applications in Biological Science and Medicine, Hooman Rashidi, 1999, CRC Press</li>
<li>Bioinformatics: Methods and Protocols (Methods in Molecular Biology, Vol 132), Stephen Misener and Stephen A. Krawetz (Editors),1999, Humana Press</li>
<li>Bioinformatics: Databases and Systems, Stanley Letovsky (Editor),1999, Kluwer Academic Publishers</li>
<li>Computational Molecular Biology, P. Green, 1998, Blackwell Science Inc.</li>
<li>Computational Methods in Molecular Biology (New Comprehensive Biochemistry, V. 32), Steven L. Salzberg, David B. Searls, Simon Kasif (Editors), 1998, Elsevier Science Ltd.</li>
<li>Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin, S. Eddy, A. Krogh, G. Mitchison, 1998, Cambridge University Press</li>
<li>Guide to Human Genome Computing, M. J. Bishop (Editor), 1998, Academic Press</li>
<li>Introduction to Computational Molecular Biology, Joao Meidanis, Joao C. Setabal, 1997, PWS Pub. Co.</li>
<li>Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Dan Gusfield, 1997, Cambridge University Press</li>
<li>Sequence Data Analysis Guidebook, Simon R. Swindell (Editor), 1997, Humana Press</li>
<li>High Performance Computational Methods for Biological Sequence Analysis, Tieng K. Yap, Ophir Frieder, Robert L. Martino, 1996, Kluwer Academic Pub.</li>
<li>Computer Methods for Macromolecular Sequence Analysis, Methods in Enzymology, volume 266, Russell F. Doolittle (Editor), 1996, Academic Press</li>
<li>DNA and Protein Sequence Analysis: A Practical Approach (Practical Approach Series , No 171), 1996, M. J. Bishop and C. J. Rawlings (Editors), 1996, IRL Press</li>
<li>Molecular Bioinformatics: Algorithms and Applications, Steffen Schulze-Kremer, 1995, Walter De Gruyter</li>
<li>Introduction to Computational Biology - Maps, sequences and genomes, Michael S. Waterman, 1995, Chapman &amp; Hall</li>
<li>Computer Analysis of Sequence Data, Annette M. Griffin and Hugh G. Griffin (Editors), 1994, Humana Press</li>
<li>Artificial Intelligence and Molecular Biology, Lawrence Hunter (Editor), 1993, AAAI Press</li>
<li>Sequence Analysis Primer, Michael Gribskov and John Devereux (Editors), 1992, Oxford University Press</li>
<li>Mathematical Methods of Analysis of Biopolymer Sequences (Dimacs Series in Discrete Mathematics and Theoretical Computer Science ; Volume 8), S. G. Gindikin, 1992, American Mathematical Society</li>
<li>Mathematical Methods for DNA Sequences, Michael S. Waterman (Editor), 1989, CRC Press</li>
</ul><p>Programming Books for Bioinformatics</p><ul>
<li>Mastering Perl for Bioinformatics, James D. Tisdall, 2003, O'Reilly</li>
<li>Genomic Perl: From Bioinformatics Basics to Working Code, Rex A. Dwyer, 2002, Cambridge University Press</li>
<li>Beginning Perl for Bioinformatics, James Tisdall, 2001, O'Reilly</li>
<li>Developing Bioinformatics Computer Skills, Cynthia Gibas, Per Jambeck, 2001, O'Reilly</li>
</ul><p>General Genomics</p><ul>
<li>Functional Microbial Genomics (Volume 33), Brendan Wren, Nick Dorrell, 2003, Academic Press</li>
<li>Discovering Genomics, Proteomics, and Bioinformatics, A. Malcolm Campbell, Laurie J. Heyer, 2002, Benjamin/Cummings</li>
<li>Genomes, Terence A. Brown, 2002, John Wiley &amp; Sons</li>
<li>Essentials of Medical Genomics, Stuart M. Brown , 2002, John Wiley &amp; Sons</li>
<li>A Primer of Genome Science, Greg Gibson, Spencer V. Muse, 2002, Sinauer Associates</li>
<li>Pathogen Genomics: Impact on Human Health, Karen Joy, Phd Shaw (Editors), 2002, Humana Press</li>
<li>Genomics, John E. Antonopoulos, 2000, Xlibris Corporation</li>
<li>Genomics and Proteomics: Functional and Computational Aspects, Sandor Suhai (Editor), 2000, Plenum Pub Corp</li>
<li>Functional Genomics: A Practical Approach (The Practical Approach Series, 235), S. Hunt and F. Livesey (Editors), 2000, Oxford Univ Press</li>
<li>Human Molecular Genetics, Andrew P. Read, Tom Strachan 1999, BIOS Scientific Publishers Ltd.</li>
<li>Genomics: The Science and Technology Behind the Human Genome Project, Charles R. Cantor and Cassandra L. Smith, 1999, John Wiley &amp; Sons</li>
<li>Cells: A Laboratory Manual, 3 volumes, David L. Spector, Robert D. Goldman, Leslie A. Leinwand, 1998, Cold Spring Harbor Laboratory Press</li>
<li>Genome Analysis: A Laboratory Manual, 4 volumes, Bruce Birren, et al. (Editors), 1997, Cold Spring Harbor Laboratory Press</li>
<li>The Human Genome Project, N. G. Cooper (Editor), 1994, University Science Books</li>
</ul><p>Comparative Genomics</p><ul>
<li>Handbook of Comparative Genomics: Principles and Methodology, Cecilia Saccone, Graziano Pesole, 2003, Wiley-Liss</li>
<li>Sequence - Evolution - Function: Computational Approaches in Comparative Genomics, Eugene V. Koonin, Michael Y. Galperin, 2002, Kluwer Academic Publishers</li>
<li>Comparative Genomics - Empirical and Analytical Approaches to Gene Order Dynamics, Map Alignment and the Evolution of Gene Families, David Sankoff and Joseph H. Nadeau, 2000, Kluwer Academic Pub</li>
<li>Comparative Genomics, Melody Clark (Editor), 2000, Kluwer Academic Pub</li>
</ul><p>Proteomics</p><ul>
<li>Proteins and Proteomics: A Laboratory Manual, Richard J. Simpson (Editor), Cold Spring Harbor Laboratory</li>
<li>Proteomics in Practice: A Laboratory Manual of Proteome Analysis , Reiner Westermeier, Tom Naven, 2002, John Wiley &amp; Sons</li>
<li>Posttranslational Modifications of Proteins: Tools for Functional Proteomics (Methods in Molecular Biology, Vol 194) , Christoph Kannicht (Editor), 2002, Humana Press</li>
<li>Peptide Arrays on Membrane Supports: Synthesis and Applications (Springer Lab Manual), Joachim Koch, Michael Mahler (Editors), 2002, Springer Verlag</li>
<li>Proteomics , Timothy Palzkill, 2002, Kluwer Academic Publishers</li>
<li>Introduction to Proteomics: Tools for the New Biology , Daniel C. Liebler (Editor), 2001, Humana Press</li>
<li>Proteome Research: Mass Spectrometry (Principles and Practice) , P. James (Editor), 2001, Springer Verlag</li>
<li>Interpreting Protein Mass Spectra: A Comprehensive Resource , A. Peter Snyder, 2000, American Chemical Society</li>
<li>Protein Sequencing and Identification Using Tandem Mass Spectrometry , Michael Kinter, Nicholas E. Sherman, 2000, Wiley-Interscience</li>
<li>From Genome to Proteome: Advances in the Practice and Application of Proteomics, Michael J. Dunn (Editor), 2000, Vch Verlagsgesellschaft Mbh</li>
<li>Proteomics: From Protein Sequence to Function, S. Pennington (Editor), M. Dunn (Editor), 2000, Springer Verlag</li>
<li>Proteome Research: Two-Dimensional Gel Electrophoresis and Detection Methods (Principles and Practice), T. Rabilloud (Editor), 2000, Springer Verlag</li>
<li>Proteome and Protein Analysis, R. M. Kamp, D. Kyriakidis, th Choli-Papadopoulou (Editor), 1999, Springer Verlag</li>
<li>Proteome Research: New Frontiers in Functional Genomics, M. R. Wilkins, et al. (Editors), 1997, Springer Verlag</li>
</ul><p>Protein Structure</p><ul>
<li>Structural Bioinformatics, Philip E. Bourne, Helge Weissig (Editors), 2003, John Wiley &amp; Sons</li>
<li>Protein Structure Prediction: Bioinfomatic Approach, I.F. Tsigelny, 2002, International University Line</li>
<li>Introduction to Protein Architecture: The Structural Biology of Proteins, Arthur M. Lesk, 2001, Oxford University Press</li>
<li>Protein Structure Prediction: Methods and Protocols, David M. Webster (Editor), 2000, Humana Press</li>
<li>Introduction to Protein Structure, Carl-Ivar Branden, John Tooze, 1999, Garland Publishing</li>
<li>Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Alan Fersht, 1999, Freeman</li>
</ul><p>Pharmacogenomics</p><ul>
<li>Pharmacogenomics: Social, Ethical, and Clinical Dimensions, Mark A. Rothstein (Editor), 2003, Wiley-Liss</li>
<li>Pharmacogenomics: The Search for Individualized Therapies, Julio Licinio, Ma-Li Wong (Editors), 2002, John Wiley &amp; Sons</li>
<li>Pharmacogenomics, Werner Kalow, Urs A. Meyer, Rachel Tyndale (Editors), 2001, Marcel Dekker</li>
<li>Pharmacogenetics and Pharmcogenomics: Recent Conceptual and Technical Advances (Pharmacology, Volume 61, Number 3, 2000), Elliot S. Vesell (Editor), 2000, S. Karger Publishing</li>
<li>Pharmacogenetics, Wendell Weber, 1997, Oxford University Press</li>
</ul><p>DNA Microarrays</p><ul>
<li>Statistical Analysis of Gene Expression Microarray Data, T. P. Speed (Editor), 2003, CRC Press</li>
<li>Microarray Gene Expression Data Analysis: A Beginner's Guide, Helen C. Causton, John Quackenbush, Alvis Brazma, 2003, Blackwell Publishers</li>
<li>The Analysis of Gene Expression Data (Statistics for Biology and Health), G. Parmigiani, E. S. Garrett, R. A. Irizarry, S. Zeger , Graeme Clark (Editors), 2003, Springer Verlag</li>
<li>A Practical Approach to Microarray Data Analysis, Daniel P. Berrar, Werner Dubitzky, Martin Granzow (Editors), 2002, Kluwer Academic Publishers</li>
<li>DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling, Pierre Baldi, G. Wesley Hatfield, 2002, Cambridge University Press</li>
<li>DNA Microarrays: A Molecular Cloning Manual, David Bowtell, Joseph Sambrook (Editors), 2002, Cold Spring Harbor Laboratory</li>
<li>DNA Array Image Analysis: Nuts &amp; Bolts, Gerda Kamberova, Shishir Shah, 2002, DNA Press</li>
<li>Microarray Analysis, Mark Schena, 2002, John Wiley &amp; Sons</li>
<li>A Biologist's Guide to Analysis of DNA Microarray Data, Steen Knudsen, 2002, John Wiley &amp; Sons</li>
<li>Microarrays for an Integrative Genomics (Computational Molecular Biology), Isaac S. Kohane, Alvin Kho, Atul J. Butte, 2002, MIT Press</li>
<li>Microarrays for the Neurosciences: An Essential Guide (Cellular and Molecular Neuroscience), Daniel H. Geschwind, Jeffrey P. Gregg (Editors), 2002, MIT Press</li>
<li>DNA Microarrays: Gene Expression Applications, Bertrand Jordan (Editor), 2001, Springer Verlag</li>
<li>DNA Arrays: Methods and Protocols (Methods in Molecular Biology, Volume 170), Jang B. Rampal (Editor), 2001, Humana Press</li>
<li>DNA Arrays: Technologies and Experimental Strategies, Elena V. Grigorenko (Editor), 2001, CRC Press</li>
<li>Microarray Biochip Technology, Mark Schena (Editor), 2000, Eaton Pub</li>
<li>Expression Genetics: Accelerated and High-Throughput Methods (Biotechniques Update Series), Michael McClelland (Editor), Arthur B. Pardee (Editor), 1999, Eaton Pub</li>
<li>DNA Microarrays: A Practical Approach (Practical Approach Series 205), Mark Schena (Editor), 1999, Oxford Univ Press</li>
<li>cDNA Preparation and Characterization (Methods in Enzymology Volume 303), S.M. Weissman (Editor), 1999, Academic Press</li>
</ul><p>Systems Biology, Genetic and Biochemical Network</p><ul>
<li>Handbook of Graphs and Networks : From the Genome to the Internet, Stefan Bornholdt, Heinz Georg Schuster (Editors), 2003, Vch Verlagsgesellschaft Mbh</li>
<li>Computational Cell Biology, Christopher Fall, Eric Marland, John Wagner, John Tyson (Editors), 2002, Springer Verlag</li>
<li>Gene Regulation and Metabolism: Post-Genomic Computational Approaches (Computational Molecular Biology), Julio Collado-Vides, Ralf Hofestadt (Editors), 2002, MIT Press</li>
<li>Foundations of Systems Biology, Hiroaki Kitano (Editor), 2001, MIT Press</li>
<li>Genomic Regulatory Systems: Development and Evolution, Eric H. Davidson , 2001, Academic Press</li>
<li>Genes &amp; Signals, Mark Ptashne, Alexander Gann, 2001, Cold Spring Harbor Laboratory</li>
<li>Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology), James M. Bower and Hamid Bolouri (Editors), 2001, MIT Press</li>
<li>Protein-Protein Interactions: A Molecular Cloning Manual, Erica Golemis (Editor), 2001, Cold Spring Harbor Laboratory</li>
<li>Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Eberhard O. Voit, 2000, Cambridge University Press</li>
<li>Mathematical Physiology, James P. Keener, James Sneyd, 1998, Springer Verlag</li>
</ul><p>&nbsp;</p><p>DNA Sequencing</p><ul>
<li>DNA Sequencing: From Experimental Methods to Bioinformatics (Introduction to Biotechniques Series), Luke Alphey, 1997, Springer Verlag</li>
<li>Automated DNA sequencing and analysis, Adams M.D., Fields C., Venter J.C. (Editors), 1994, Academic Press</li>
</ul><p>&nbsp;</p><p>Apart from above mentioned books, you can also find some useful books links at following mentioned URLs:</p><p>&nbsp;</p><p><a href="http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713">http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713</a></p><p><a href="http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545">http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545</a></p><p><a href="http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068">http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068</a></p><p><a href="http://books.google.no/books?id=pxSM7R1sdeQC&amp;dq=Pierre+baldi+%2B+bioinformatics&amp;printsec=frontcover&amp;source=bn&amp;hl=en&amp;ei=IoGRS6uCIJT-NYLA8Z0N&amp;sa=X&amp;oi=book_result&amp;ct=result&amp;redir_esc=y#v=onepage&amp;q&amp;f=false">http://books.google.no/books?id=pxSM7R1sdeQC&amp;dq=Pierre+baldi+%2B+bioinformatics&amp;printsec=frontcover&amp;source=bn&amp;hl=en&amp;ei=IoGRS6uCIJT-NYLA8Z0N&amp;sa=X&amp;oi=book_result&amp;ct=result&amp;redir_esc=y#v=onepage&amp;q&amp;f=false</a></p><p><a href="http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826">http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826</a></p><p>&nbsp;</p><p>If you think your favourite books are not listed then please write it down in comment section for the benefits of other users.&nbsp;Feel free to add many more books in comment section.&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2728/statistics-of-current-sequencing-and-bioinformatics-market</guid>
	<pubDate>Wed, 21 Aug 2013 08:29:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2728/statistics-of-current-sequencing-and-bioinformatics-market</link>
	<title><![CDATA[Statistics of current Sequencing and Bioinformatics market]]></title>
	<description><![CDATA[<p>This survey conducted by&nbsp;<strong>Oxford&nbsp;<a href="http://www.ogt.co.uk/" target="_blank">Gene</a>&nbsp;Technology,</strong>&nbsp;<span>provider of innovative&nbsp;genetics&nbsp;research and&nbsp;biomarker</span>&nbsp;<span>solutions to advance molecular medicine, has released the results from a recent survey of researchers using next generation sequencing. (Source:<a href="http://www.news-medical.net/news/20130821/Oxford-Gene-Technology-releases-next-generation-sequencing-survey-results.aspx">http://www.news-medical.net/news/20130821/Oxford-Gene-Technology-releases-next-generation-sequencing-survey-results.aspx</a>&nbsp;)</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://www.ogt.com/assets/0000/3190/NGS_Survey_2013_Infographic_Web.pdf" rel="nofollow">http://www.ogt.com/assets/0000/3190/NGS_Survey_2013_Infographic_Web.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4835/chang-lab</guid>
  <pubDate>Tue, 24 Sep 2013 17:25:49 -0500</pubDate>
  <link></link>
  <title><![CDATA[Chang lab]]></title>
  <description><![CDATA[
<p>The Chang lab is focused on how the activities of hundreds or even thousands of genes (gene parties) are coordinated to achieve biological meaning. We have pioneered methods to predict, dissect, and control large-scale gene regulatory programs; these methods have provided insights into human development, cancer, and aging. A particular interest is how cells know and remember their locations in the body, particularly with the help of long noncoding RNAs.</p>

<p>More at http://changlab.stanford.edu/index.html</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/3031/following-the-scientific-literature-a-personal-practical-guide-for-young-computational-biologists</guid>
	<pubDate>Fri, 23 Aug 2013 07:18:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/3031/following-the-scientific-literature-a-personal-practical-guide-for-young-computational-biologists</link>
	<title><![CDATA[Following the scientific literature: A personal practical guide for young computational biologists]]></title>
	<description><![CDATA[<p><span>The goal of this guide is to describe&nbsp;</span><strong>why</strong><span>,&nbsp;</span><strong>when</strong><span>,&nbsp;</span><strong>where</strong><span>&nbsp;and&nbsp;</span><strong>how</strong><span>&nbsp;can you follow the most up-to-date science of interest and&nbsp;</span><strong>what</strong><span>&nbsp;papers/journals you should follow. The guide is biased towards the fields of genomics/systems biology.(from article)</span></p>
<p><span>Source:&nbsp;<strong><span>&nbsp;<a href="http://www.cs.tau.ac.il/~ulitskyi/">Igor Ulitsky</a>&nbsp;&amp;&nbsp;<a href="http://www.cs.tau.ac.il/~rshamir/">Ron Shamir</a></span></strong></span></p><p>Address of the bookmark: <a href="http://acgt.cs.tau.ac.il/guides/LiteratureGuide.htm" rel="nofollow">http://acgt.cs.tau.ac.il/guides/LiteratureGuide.htm</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/4314/postdocs-positions-in-computer-science-in-helsinki-finland</guid>
  <pubDate>Fri, 06 Sep 2013 10:11:19 -0500</pubDate>
  <link></link>
  <title><![CDATA[PostDocs positions in computer science in HELSINKI, FINLAND]]></title>
  <description><![CDATA[
<p>Several university departments in the Helsinki region, Finland, are looking for postdoctoral researchers in the field of computer science and information technology. Jobs are available at:<br />·       Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, http://www.hiit.fi<br />·       Department of Computer Science, University of Helsinki, http://www.cs.helsinki.fi<br />·       Department of Information and Computer Science, Aalto University, http://ics.aalto.fi<br />·       Department of Computer Science and Engineering, Aalto University, http://cse.aalto.fi<br />·       Department of Mathematics and Statistics, University of Helsinki, http://mathstat.helsinki.fi/english/<br /> <br />Why Helsinki?<br />The collaborating Aalto University and University of Helsinki form a leading hub of computer science and modelling, including Machine learning, Data mining, Algorithms, Computational Logic, Cloud computing, Distributed computing, Human-centric ubiquitous ICT, Bioinformatics, etc.<br />Helsinki region is a safe, pleasant and attractive place to live in, with well-functioning services such as public transport etc. Finland has a comprehensive social security and health care system, including exceptionally good parental leaves, and children's day care services.<br /> <br />Positions are offered in:<br />Algorithm engineering (String Algorithms group)<br />Algorithmic bioinformatics (Genome-Scale Algorithmics group)<br />Automated reasoning and search, especially propositional logic (Computational Logic group)<br />Computational astrophysics and/or data analysis (Computational Methods and Data Analysis for Astrophysics group)<br />Computational biology and statistical methods in bioinformatics (Computational Systems Biology group)<br />Computational creativity and data mining (Discovery group)<br />Dynamic and large-scale networked systems (Data Communications Software group)<br />Intelligent multimodal information access (Content-Based Image and Information Retrieval Group)<br />Machine learning and neuroscience (Statistical Machine Learning group)<br />Machine learning for structured data (Kernel Machines, Pattern Analysis and Computational Biology group)<br />Machine learning methods for infectious disease epidemiology (Bayesian Statistics Group)<br />Probabilistic modeling and machine learning (Complex Systems Computation group)<br />Statistical machine learning (Statistical Machine Learning group)<br />Analysing ubiquitous sensor data (HIIT-Wide Focus Area)<br />Interactive visualization (HIIT-Wide Focus Area)<br />Affective computing and BCI (HIIT-Wide Focus Area)<br />Intelligent user interfaces and/or recommender systems (HIIT-Wide Focus Area)<br />Information retrieval and HCI (HIIT-Wide Focus Area)<br />Machine learning and data analysis, especially information retrieval, HCI, text and context data (HIIT-Wide Focus Area)<br />Probabilistic modeling and data analysis for bioinformatics (HIIT-Wide Focus Area)</p>

<p>More at http://www.hiit.fi/postdoc-call-2013</p>
]]></description>
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