<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/12883?offset=30 https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines Sat, 21 May 2016 22:42:16 -0500 https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines <![CDATA[Bpipe - a tool for running and managing bioinformatics pipelines]]> Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome Mon, 04 Jul 2016 00:44:55 -0500 https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome <![CDATA[4DGenome]]> Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies. 

All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology. 

Address of the bookmark: http://4dgenome.research.chop.edu/

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/30236/pyscaf Mon, 19 Dec 2016 14:20:33 -0600 https://bioinformaticsonline.com/bookmarks/view/30236/pyscaf <![CDATA[pyScaf]]> pyScaf orders contigs from genome assemblies utilising several types of information:

Scaffolding 

In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.

Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:

  • matches not satisfying cut-offs (--identity and --overlap)
  • suboptimal matches (only best match of each query to reference is kept)
  • and removing overlapping matches on reference.

In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on C. parapsilosis (13 Mb; CANPA) and A. thaliana (119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (Figures in dropbox, CANPA table, ARATH table).
Runs took ~0.5 min for CANPA on 4 CPUs and ~2 min for ARATH on 16 CPUs.

Important remarks:

  • Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.
  • pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no de novo assembler / scaffolder is perfect...), which breaks synteny.
  • pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.
  • pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations. Note however, this is experimental implementation!
  • Consider closing gaps after scaffolding.

Address of the bookmark: https://github.com/lpryszcz/pyScaf

]]> Bulbul https://bioinformaticsonline.com/bookmarks/view/30897/finestructure-v2-globetrotter Mon, 13 Feb 2017 08:40:23 -0600 https://bioinformaticsonline.com/bookmarks/view/30897/finestructure-v2-globetrotter <![CDATA[fineSTRUCTURE v2 & GLOBETROTTER]]> Software available at this site

  • FineSTRUCTURE version 2, a pipeline for running ChromoPainter and FineSTRUCTURE for population inference. A GUI is available for interpretation. Download from the Downloads page.
  • FineSTRUCTURE R scripts, a facility for exploring the results when the GUI is unavailable.
  • GLOBETROTTER, the admixture dating method based on ChromoPainter. Download from the Downloads page.
  • AdmixturePainting, A set of R tools to inmterpret the results of ADMIXTURE and STRUCTURE-like mixture models.
  • RADpainter, finestructure and ChromoPainter for RAD tag data used for non-model organisms.
  • Scripts to perform many types of conversion. Included in the main software download from the Downloads page.
What this page is This page provides information about and downloads for methodology for Chromosome Painting. It is not a facility to analyse your genome. Sorry if you were misled by the punchy name!
About Chromosome Painting Painting is an efficient way of identifying important haplotype information from dense genotype data. It describes ancestry in an efficient way suitable for a range of further analyses, including population identification and admixture dating.

Address of the bookmark: http://paintmychromosomes.com/

]]> Shruti Paniwala https://bioinformaticsonline.com/bookmarks/view/31012/genomecomp Fri, 17 Feb 2017 08:38:32 -0600 https://bioinformaticsonline.com/bookmarks/view/31012/genomecomp <![CDATA[GenomeComp]]> GenomeComp is a tool for summarizing, parsing and visualizing the genome wide sequence comparison results derived from voluminous BLAST textual output, so as to locate the rearrangements, insertions or deletions of genome segments between species or strains.

It can be easily used to compare, parsing and visualize large genomic sequences, especially closely related genomes such as inter-species or inter-strains. In addition, it can also show other sequence features like repeat sequence distributions in one whole-genome DNA sequence by comparing the genome to itself.

It is a stand-alone graphical user interface (GUI) program which runs on Linux, Unix, Mac OS X (tested on version 10.2.4 only) and Microsoft Windows platforms and is written in Perl/Tk.

Address of the bookmark: http://www.mgc.ac.cn/GenomeComp/

]]> Jit https://bioinformaticsonline.com/opportunity/view/43272/bioinformatics-head-bioinformatics-manager-iii-cancer-genomics-research-laboratory-at-frederick-national-laboratory Wed, 18 Aug 2021 00:19:48 -0500 <![CDATA[Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory]]> Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

]]> https://bioinformaticsonline.com/researchlabs/view/12870/nuclear-dynamics-lab Thu, 17 Jul 2014 15:03:27 -0500 <![CDATA[Nuclear Dynamics Lab]]> Lab focus is to elucidate fundamental principles, new mechanisms, machineries and emergent properties that are involved in maintaining the genome and gene expression programmes for improvements in lifelong health and well-being for all.

More at http://www.babraham.ac.uk/our-research/nuclear-dynamics/

]]> https://bioinformaticsonline.com/researchlabs/view/19648/mit-computational-biology-group Thu, 18 Dec 2014 14:47:01 -0600 <![CDATA[MIT Computational Biology Group]]> My research group consists primarily of computer science graduate students and postdocs with expertise in algorithms, statistical inferences and machine learning, and sharing a passion for understanding fundamental biological problems.

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

]]> https://bioinformaticsonline.com/bookmarks/view/19786/shrec3d Thu, 25 Dec 2014 23:14:52 -0600 https://bioinformaticsonline.com/bookmarks/view/19786/shrec3d <![CDATA[ShRec3D]]> ShRec3D is a program that aims at reconstructing a genome 3D structure (b) from the sole knowledge of the contacts between different genomic regions (a) as determined by Hi-C (http://www.ncbi.nlm.nih.gov/pubmed/19815776).

There are two options to run ShRec3D (on linuX only so far): the first one uses the Matlab complier runtime environment (MCR), the second one doesn't need any other library to be installed but only works with the latest versions of Linux (equivalent to Fedora 19 and above).

Address of the bookmark: https://sites.google.com/site/julienmozziconacci/#TOC-Downloads

]]> Jitendra Narayan https://bioinformaticsonline.com/researchlabs/view/26828/bioinfolab Fri, 25 Mar 2016 11:05:35 -0500 <![CDATA[BioinfoLab]]> Laboratory of Statistics and Computational tools for Bioinformatics

The Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab) is hosted at the Istituto per le Applicazioni del Calcolo "Mauro Picone" - CNR . The laboratory has been officially opened in 2012 with the support of Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013 (PON "R&C"), and it incorporates several expertise and research activities started since 2007, and supported by several CNR projects. Main interest of BioinfoLab is to develop novel statistical methods and computational tools for the analysis of high dimensional data arising from "Multi-omics" applications. In particular, current activities involve the analysis of ChIP-seq and RNA-seq experiments.

More at http://bioinfo.na.iac.cnr.it/BioinfoLab/index.html

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