<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/30144?offset=360 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/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads Fri, 19 Oct 2018 07:25:21 -0500 https://bioinformaticsonline.com/bookmarks/view/37957/base-a-practical-de-novo-assembler-for-large-genomes-using-long-ngs-reads <![CDATA[BASE: a practical de novo assembler for large genomes using long NGS reads]]> new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.

Address of the bookmark: https://github.com/dhlbh/BASE

]]> Rahul Nayak 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/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore Mon, 12 Nov 2018 05:26:48 -0600 https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore <![CDATA[Pacasus: Correction of palindromes in long reads from PacBio and Nanopore]]>
Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder: https://github.com/swarris/Pacasus/blob/master/vib2017.pdf.

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r Fri, 13 Aug 2021 07:44:05 -0500 https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r <![CDATA[QuasR: Quantification and annotation of short reads in R]]> The QuasR package (short for Quantify and annotate short reads in R) integrates the functionality of several R packages (such as IRanges (Lawrence et al. 2013) and Rsamtools) and external software (e.g. bowtie, through the Rbowtie package, and HISAT2, through the Rhisat2 package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single R script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

]]> Neel https://bioinformaticsonline.com/researchlabs/view/22410/nicolas-corradi-lab Tue, 26 May 2015 16:19:02 -0500 <![CDATA[Nicolas Corradi Lab]]> The goal of our research is to better understand the biology of microbial organisms of significant ecological, veterinary and medical importance.
To achieve this goal, our team combines the power of next generation DNA sequencing and bioinformatics with molecular biology and experimental procedures.

Main research topics:
- Comparative and Population Genomics of Plant Symbionts
- Parasite Genome Evolution
- Experimental Evolution of Microbial Symbionts and Parasites
- Phylogenomics of Early Branching Fungi

More at http://corradilab.weebly.com/

]]> https://bioinformaticsonline.com/opportunity/view/24298/staff-scientists-at-national-institute-of-plant-genome-research-new-delhi Fri, 04 Sep 2015 22:06:59 -0500 <![CDATA[Staff Scientists at National Institute of Plant Genome Research, New Delhi]]> National Institute of Plant Genome Research, New Delhi is an Autonomous Research Institution funded by Department of Biotechnology, Ministry of Science & Technology, Govt. of India, to pursue research on various aspects of plant genomics. The Institute is also in the process of establishing a NIPGR Translational Centre at Biotech Science Cluster, NCR, Faridabad. NIPGR invites applications from Indian Citizens for filling up the vacant posts on Direct Recruitment basis, as detailed below. The posts are temporary but likely to continue.

Staff Scientists

Specialization: Applicant should have a Ph.D. with excellent academic credentials along with the track record of scientific productivity evidenced by publications/patents/products in the frontier areas of Plant Biology such as, Computational Biology, Genome Analysis and Molecular Mapping, Molecular Mechanism of Abiotic Stress Responses, Nutritional Genomics, Plant Development and Architecture, Plant Immunity, Molecular Breeding, Transgenics for crop improvement and other emerging areas based on plant genomics.

Remuneration: The length of experience and scientific accomplishments/quality of scientific productivity record will be major factors in deciding the level of appointment as Staff Scientist as well as starting salary in the Pay Bands of Rs 15,600-39,100 (with grade pay of 5400), and Rs 37,400-67,000 (with grade pay of 8,700 and 8,900) plus usual allowances admissible to the Central Government employees. However, NIPGR reserves the right to select candidates in the lower grade against the foregoing posts depending upon the qualifications and experience of the candidate. Reservation of posts shall be as per Govt. of India norms. Five posts (SC-2, ST-1, OBC-2) in the Pay Band of Rs 15,600-39,100 with Grade Pay of Rs 5400, are reserved.

More at http://www.nipgr.res.in/careers/vacancies_latest.php#

Apply online at http://www.nipgr.res.in/nipgr_recu/nipgr_recu.php

Form http://www.nipgr.res.in/files/careers/Application_Performa_2015.doc

]]> https://bioinformaticsonline.com/bookmarks/view/26587/last Wed, 09 Mar 2016 14:27:01 -0600 https://bioinformaticsonline.com/bookmarks/view/26587/last <![CDATA[LAST]]> sketch of similar regions in sequences

LAST can:

  • Handle big sequence data, e.g:
    • Compare two vertebrate genomes
    • Align billions of DNA reads to a genome
  • Indicate the reliability of each aligned column.
  • Use sequence quality data properly.
  • Compare DNA to proteins, with frameshifts.
  • Compare PSSMs to sequences
  • Calculate the likelihood of chance similarities between random sequences.
  • Do split and spliced alignment.
  • Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

]]> Archana Malhotra 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

]]> https://bioinformaticsonline.com/bookmarks/view/26319/n50plottingtools Mon, 08 Feb 2016 15:39:04 -0600 https://bioinformaticsonline.com/bookmarks/view/26319/n50plottingtools <![CDATA[n50PlottingTools]]> Tools to create plots showing N-statistics for genome assemblies

More at https://github.com/dentearl/n50PlottingTools

Address of the bookmark: https://github.com/dentearl/n50PlottingTools

]]> Jit