<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/28915?offset=420 https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning Fri, 01 Jul 2016 12:57:12 -0500 https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning <![CDATA[Machine Learning !!!]]> In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

]]> Gudiya Pal https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools Thu, 03 Nov 2016 08:28:25 -0500 https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools <![CDATA[ART: Set of Simulation Tools]]> ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the 1000 Genomes Project . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. VarSim) for evaluating variant calling tools or methods.

Address of the bookmark: http://www.niehs.nih.gov/research/resources/software/biostatistics/art/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/30012/swalo Wed, 30 Nov 2016 05:06:05 -0600 https://bioinformaticsonline.com/bookmarks/view/30012/swalo <![CDATA[SWALO]]> SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.

Download

Git repository of SWALO is at https://github.com/atifrahman/SWALO.

Address of the bookmark: https://atifrahman.github.io/SWALO/

]]> Jit https://bioinformaticsonline.com/file/view/29652/bioistats-ppt Tue, 08 Nov 2016 07:09:01 -0600 https://bioinformaticsonline.com/file/view/29652/bioistats-ppt <![CDATA[Bioistats PPT]]> Basics concepts of Probability: The Study of Randomness

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.

]]> Jit https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics Wed, 09 Nov 2016 16:29:24 -0600 https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics <![CDATA[Method in Comparative genomics !!]]> 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.

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.

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.

Address of the bookmark: http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf

]]> Jit https://bioinformaticsonline.com/bookmarks/view/29992/spines Mon, 28 Nov 2016 05:33:26 -0600 https://bioinformaticsonline.com/bookmarks/view/29992/spines <![CDATA[Spines]]> Spines 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: Satsuma, a highly parallelized program for high-sensitivity, genome-wide synteny; Papaya, an all-purpose alignment tool for less diverged sequences; and SLAP, a context-sensitive local aligner for diverged sequences with large gaps.

Access Spines here.

Address of the bookmark: https://www.broadinstitute.org/genome-sequencing-and-analysis/spines

]]> Jit https://bioinformaticsonline.com/bookmarks/view/30168/gene-synteny-database Fri, 16 Dec 2016 11:09:39 -0600 https://bioinformaticsonline.com/bookmarks/view/30168/gene-synteny-database <![CDATA[Gene Synteny Database]]> 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.

Availability: Genomicus is freely available for online use at http://www.dyogen.ens.fr/genomicus while data can be downloaded at ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus

Contact: rf.sne.eigoloib@crh

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853686/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/32011/fools-guide Sun, 02 Apr 2017 14:31:18 -0500 https://bioinformaticsonline.com/bookmarks/view/32011/fools-guide <![CDATA[Fools guide]]> This website and accompaning documents are intended as a tool to help researchers dealing with non-model organisms acquire and process transcriptomic high-throughput sequencing data without having to learn extensive bioinformatics skills. It covers all steps from tissue collection, sample preparation and computer setup, through addressing biological questions with gene expression and SNP data.

http://sfg.stanford.edu/denovo.html

http://sfg.stanford.edu/sequencing.html

http://sfg.stanford.edu/BLAST.html

http://sfg.stanford.edu/denovo.html 

Address of the bookmark: http://sfg.stanford.edu/guide.html

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/30833/dnasp-v5-a-software-for-comprehensive-analysis-of-dna-polymorphism-data Mon, 06 Feb 2017 04:45:37 -0600 https://bioinformaticsonline.com/bookmarks/view/30833/dnasp-v5-a-software-for-comprehensive-analysis-of-dna-polymorphism-data <![CDATA[DnaSP v5: a software for comprehensive analysis of DNA polymorphism data]]> DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Address of the bookmark: http://www.ub.edu/dnasp/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 Mon, 10 Apr 2017 04:45:40 -0500 https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 <![CDATA[WGS Celera Assembler version 8.3rc2]]> These are release notes for Celera Assembler version 8.3rc2, which was released on May 24, 2015.

This distribution package provides a stable, tested, documented version of the software.  The distribution is usable on most Unix-like platforms, and some platforms have pre-compiled binary distributions ready for installation.

The source code package includes full source code (revision 4627), Makefiles, and scripts.  A subset of the kmer package (http://kmer.sourceforge.net/, version r1994), used by some modules of Celera Assembler, is included.  This distribution includes [http://samtools.sourceforge.net/ SAMtools], [http://www.cbcb.umd.edu/software/jellyfish/ Jellyfish 2.0], [https://github.com/pbjd/pbutgcns PBUTGCNS], [https://github.com/PacificBiosciences/pbdagcon PBDAGCON], [https://github.com/PacificBiosciences/BLASR BLASR], and parts of the [https://github.com/PacificBiosciences/FALCON/tree/v0.1.3 Falcon assembler].

Full documentation can be found online at http://wgs-assembler.sourceforge.net/.

Interesting scripts within it

urbe@urbo214b[bin] ls                                                        []
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-rwxrwxr-x 1 urbe urbe 124K Apr 10 11:40 fastqAnalyze
-rwxrwxr-x 1 urbe urbe 137K Apr 10 11:40 fastqSample
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lrwxrwxrwx 1 urbe urbe    4 Apr 10 11:41 pacBioToCA -> PBcR
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drwxrwxr-x 2 urbe urbe 4,0K Apr 10 11:41 TIGR
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Address of the bookmark: http://wgs-assembler.sourceforge.net/wiki/index.php/Main_Page

]]> Jit