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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37239?offset=270</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator</guid>
	<pubDate>Fri, 26 Jul 2019 00:58:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator</link>
	<title><![CDATA[jackalope: A swift, versatile phylogenomic and high-throughput sequencing simulator]]></title>
	<description><![CDATA[<p><code>jackalope</code> simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations&mdash;the latter of which can include selection, recombination, and demographic fluctuations. <code>jackalope</code> can simulate single, paired-end, or mate-pair Illumina reads, as well as reads from Pacific Biosciences These simulations include sequencing errors, mapping qualities, multiplexing, and optical/PCR duplicates. All outputs can be written to standard file formats.</p>
<p><span>A swift, versatile phylogenomic and high-throughput sequencing simulator </span> <span><a href="https://jackalope.lucasnell.com">https://jackalope.lucasnell.com</a></span></p><p>Address of the bookmark: <a href="https://github.com/lucasnell/jackalope" rel="nofollow">https://github.com/lucasnell/jackalope</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links</guid>
	<pubDate>Thu, 13 Feb 2020 00:20:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links</link>
	<title><![CDATA[genomics public data links !]]></title>
	<description><![CDATA[<p>List of publically available databases on google server.</p>
<p>More at <a href="https://software.broadinstitute.org/gatk/download/bundle">https://software.broadinstitute.org/gatk/download/bundle</a></p>
<p><a href="ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/">ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/</a>.</p>
<p><a href="ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/">ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/</a></p><p>Address of the bookmark: <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1" rel="nofollow">https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36736/checkmassessing-the-quality-of-microbial-genomes-recovered-from-isolates-single-cells-and-metagenomes</guid>
	<pubDate>Wed, 23 May 2018 04:39:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36736/checkmassessing-the-quality-of-microbial-genomes-recovered-from-isolates-single-cells-and-metagenomes</link>
	<title><![CDATA[CheckM:Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes]]></title>
	<description><![CDATA[<p><span>CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes. It provides robust estimates of genome completeness and contamination by using collocated sets of genes that are ubiquitous and single-copy within a phylogenetic lineage. Assessment of genome quality can also be examined using plots depicting key genomic characteristics (e.g., GC, coding density) which highlight sequences outside the expected distributions of a typical genome. CheckM also provides tools for identifying genome bins that are likely candidates for merging based on marker set compatibility, similarity in genomic characteristics, and proximity within a reference genome tree.</span></p><p>Address of the bookmark: <a href="http://ecogenomics.github.io/CheckM/" rel="nofollow">http://ecogenomics.github.io/CheckM/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26852/awesome-bioinformatics-pipelines</guid>
	<pubDate>Wed, 30 Mar 2016 21:50:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26852/awesome-bioinformatics-pipelines</link>
	<title><![CDATA[Awesome bioinformatics pipelines !]]></title>
	<description><![CDATA[<p><span>A curated list of awesome pipeline toolkits ...</span></p>
<p><span>https://github.com/pditommaso/awesome-pipeline</span></p><p>Address of the bookmark: <a href="https://github.com/pditommaso/awesome-pipeline" rel="nofollow">https://github.com/pditommaso/awesome-pipeline</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38747/seqkit-a-cross-platform-and-ultrafast-toolkit-for-fastaq-file-manipulation</guid>
	<pubDate>Sun, 20 Jan 2019 06:18:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38747/seqkit-a-cross-platform-and-ultrafast-toolkit-for-fastaq-file-manipulation</link>
	<title><![CDATA[SeqKit - a cross-platform and ultrafast toolkit for FASTA/Q file manipulation]]></title>
	<description><![CDATA[<p><span>FASTA and FASTQ are basic and ubiquitous formats for storing nucleotide and protein sequences. Common manipulations of FASTA/Q file include converting, searching, filtering, deduplication, splitting, shuffling, and sampling. Existing tools only implement some of these manipulations, and not particularly efficiently, and some are only available for certain operating systems. Furthermore, the complicated installation process of required packages and running environments can render these programs less user friendly.</span></p><p>Address of the bookmark: <a href="https://bioinf.shenwei.me/seqkit/usage/" rel="nofollow">https://bioinf.shenwei.me/seqkit/usage/</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/13226/you-and-your-friend-have-similar-dna</guid>
	<pubDate>Sun, 27 Jul 2014 20:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/13226/you-and-your-friend-have-similar-dna</link>
	<title><![CDATA[You and your friend have similar DNA !!!]]></title>
	<description><![CDATA[<p>New research out of Massachusetts claims that people often choose friends that are similar to them in genetics and they are more accurate than you might suppose. A study published on PNAS&nbsp;http://www.pnas.org/content/111/Supplement_3/10796.full found that people are apt to pick friends who are genetically similar to themselves - so much so that friends tend to be as alike at the genetic level as a person's fourth cousin.</p><div style="text-align: center;"><img src="http://i.kinja-img.com/gawker-media/image/upload/s--CwLwHa43--/18fbmlokxcmqcjpg.jpg" alt="image" width="300" height="271" style="border: 0px; border: 0px;"></div><p>Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes &mdash; a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population.&nbsp;</p><p>The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.</p><p>Reference:</p><p>http://www.pnas.org/content/111/Supplement_3/10796.full</p><p>Image : http://i.kinja-img.com</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29652/bioistats-ppt</guid>
	<pubDate>Tue, 08 Nov 2016 07:09:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29652/bioistats-ppt</link>
	<title><![CDATA[Bioistats PPT]]></title>
	<description><![CDATA[<p>Basics concepts of&nbsp;Probability: The Study of Randomness</p><p>Biostatistics is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch of this is medical biostatistics, which is exclusively concerned with medicine and health.</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29652" length="1663809" type="application/pdf" />
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics</guid>
	<pubDate>Wed, 09 Nov 2016 16:29:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics</link>
	<title><![CDATA[Method in Comparative genomics !!]]></title>
	<description><![CDATA[<p>We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change.</p>
<p>We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.</p>
<p>Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.</p><p>Address of the bookmark: <a href="http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf" rel="nofollow">http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29992/spines</guid>
	<pubDate>Mon, 28 Nov 2016 05:33:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29992/spines</link>
	<title><![CDATA[Spines]]></title>
	<description><![CDATA[<p><a href="https://www.broadinstitute.org/ftp/distribution/software/spines/"><em>Spines</em></a>&nbsp;is a collection of software tools, developed and used by the Vertebrate Genome Biology Group at the Broad Institute. It provides basic data structures for efficient data manipulation (mostly genomic sequences, alignments, variation etc.), as well as specialized tool sets for various analyses. It also features three sequence alignment packages:&nbsp;<em>Satsuma,</em>&nbsp;a highly parallelized program for high-sensitivity, genome-wide synteny;&nbsp;<em>Papaya,</em>&nbsp;an all-purpose alignment tool for less diverged sequences; and&nbsp;<em>SLAP,</em>&nbsp;a context-sensitive local aligner for diverged sequences with large gaps.</p>
<p>Access&nbsp;<em>Spines</em>&nbsp;<a href="https://www.broadinstitute.org/ftp/distribution/software/spines/">here</a>.</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/genome-sequencing-and-analysis/spines" rel="nofollow">https://www.broadinstitute.org/genome-sequencing-and-analysis/spines</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30168/gene-synteny-database</guid>
	<pubDate>Fri, 16 Dec 2016 11:09:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30168/gene-synteny-database</link>
	<title><![CDATA[Gene Synteny Database]]></title>
	<description><![CDATA[<p>Comparative genomics remains a pivotal strategy to study the evolution of gene organization, and this primacy is reinforced by the growing number of full genome sequences available in public repositories. Despite this growth, bioinformatic tools available to visualize and compare genomes and to infer evolutionary events remain restricted to two or three genomes at a time, thus limiting the breadth and the nature of the question that can be investigated. Here we present Genomicus, a new synteny browser that can represent and compare unlimited numbers of genomes in a broad phylogenetic view. In addition, Genomicus includes reconstructed ancestral gene organization, thus greatly facilitating the interpretation of the data.</p>
<p><strong>Availability:</strong>&nbsp;Genomicus is freely available for online use at&nbsp;<a href="http://www.dyogen.ens.fr/genomicus" target="pmc_ext">http://www.dyogen.ens.fr/genomicus</a>&nbsp;while data can be downloaded at&nbsp;<a href="ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus" target="pmc_ext">ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus</a></p>
<p><strong>Contact:</strong>&nbsp;<a href="mailto:dev@null">rf.sne.eigoloib@crh</a></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853686/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853686/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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