BOL: Related items

MeDuSa: a multi-draft based scaffolder

Abhimanyu Singh — Wed, 14 Feb 2018 02:49:00 -0600

MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalises the scaffolding problem by means of a combinatorial optimisation formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries.

Address of the bookmark: https://github.com/combogenomics/medusa

LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)

Jit — Mon, 14 May 2018 04:26:30 -0500

LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly.

Further information about LACHESIS, including source code, documentation and a user's guide are available at: http://shendurelab.github.io/LACHESIS.

Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology 2013 Dec;31(12):1119-25. doi: 10.1038/nbt.272. PubMed PMID: 24185095.

http://shendurelab.github.io/LACHESIS/

Address of the bookmark: http://shendurelab.github.io/LACHESIS/

A comprehensive atlas of human gene activity released !!!

Abhimanyu Singh — Tue, 02 Sep 2014 14:20:24 -0500

A large international consortium of researchers has produced the first comprehensive, detailed map of the way genes work across the major cells and tissues of the human body. The findings describe the complex networks that govern gene activity, and the new information could play a crucial role in identifying the genes involved with disease.

We are able to pinpoint the regions of the genome that can be active in a disease and in normal activity, whether it’s in a brain cell, the skin, in blood stem cells or in hair follicles. This is a major advance that will greatly increase our ability to understand the causes of disease across the body.

The research is outlined in a series of papers published March 27, 2014, two in the journal Nature and 16 in other scholarly journals. The work is the result of years of concerted effort among 250 experts from more than 20 countries as part of FANTOM 5 (Functional Annotation of the Mammalian Genome). The FANTOM project, led by the Japanese institution RIKEN, is aimed at building a complete library of human genes.

Researchers studied human and mouse cells using a new technology called Cap Analysis of Gene Expression (CAGE), developed at RIKEN, to discover how 95% of all human genes are switched on and off. These “switches” — called “promoters” and “enhancers” — are the regions of DNA that manage gene activity. The researchers mapped the activity of 180,000 promoters and 44,000 enhancers across a wide range of human cell types and tissues and, in most cases, found they were linked with specific cell types.

Referene : www.kurzweilai.net/first-comprehensive-atlas-of-human-gene-activity-released

Phased Human Genome Assembly !

Rahul Nayak — Mon, 08 Oct 2018 09:10:54 -0500

The new publicly available assembly (PacBio HG00733) has the fewest gaps of any human genome assembly, with more than half of the genome contained in gapless sequence at least 27 Mb long. The primary contig assembly is 2.89 Gb long and consists of 865 contigs that were assembled with PacBio data generated with the company’s Sequel® System. Using the FALCON-Unzip assembler, maternal and paternal haplotypes were resolved over more than 80% of the genome. Maternal and paternal haplotype blocks were then further phased using Hi-C technology and the FALCON-Phase methoddeveloped in collaboration with Phase Genomics. The genome was then de novo scaffolded using Phase Genomics’ Proximo Hi-C platform, resulting in the first chromosome-scale diploid assembly of a single individual accomplished with only two technologies. More specific details about the assembly are included on the PacBio blog.

The data are available using NCBI accession IDs: BioProject: (PRJNA483067), assembly: [RBJD00000000] and sequence data (SRP155659).

Additional Resources

Interactive map showcasing global initiatives underway to generate reference-quality human genome assemblies for diverse populations
BioReport Podcast on the value of ethnic-specific reference genomes
Nature Reviews Genetics paper from NHGRI: Prioritizing diversity in human genomics research
Article in The Journal of Precision Medicine: “Minority Report – Ethnic Diversity and the Real Promise for Precision Medicine”
Article in Bio-IT World: “Genomic Data Standards Are a Necessity”
NHGRI Project Award: High Quality Human and Non-Human Primate Genome Assemblies

More details are available on the PacBio website:

Blog post: Data Release: Highest-Quality, Most Contiguous Individual Human Genome Assembly to Date
Blog post: For Reference-Grade Human Genome Assemblies, SMRT Sequencing Yields Optimal Results
Webinar: Assembling High-Quality Human Reference Genomes for Global Populations
FALCON-Phase press release and article preprint
PacBio research focus webpage about Human Population Genetics

Ref: https://stockguru.com/2018/10/08/pacific-biosciences-releases-highest-quality-most-contiguous-individual-human-genome-assembly-to-date/

Referee: Genome assembly quality scores

Jit — Sun, 04 Nov 2018 16:44:30 -0600

Modern genome sequencing technologies provide a succint measure of quality at each position in every read, however all of this information is lost in the assembly process. Referee summarizes the quality information from the reads that map to a site in an assembled genome to calculate a quality score for each position in the genome assembly.

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

Orange-Bioinformatics 2.5.34

Robert M Willioms — Mon, 06 Oct 2014 12:51:37 -0500

Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

Biinformamatics Lead at Google Life Sciences

Fri, 17 Oct 2014 02:24:55 -0500

Google Life Sciences is recruiting a technical lead with experience in bioinformatics and clinical bioinformatics, including for biomarker discovery projects such as the Baseline study.

Responsibilities

Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.
Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.
Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.
Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.
Create scientifically rigorous visualizations, communications, and presentations of results.

Reference @ https://www.google.com/about/careers/search#!t=jo&jid=62095001

Vital-IT

Abhi — Thu, 18 Dec 2014 10:46:59 -0600

Vital-IT is a bioinformatics competence center that supports and collaborates with life scientists in Switzerland and beyond. The multi-disciplinary team provides expertise, training and maintains a high-performance computing (HPC) and storage infrastructure, so as to help develop, maintain and extend life science and medical research (activities).

Address of the bookmark: http://www.vital-it.ch/

MIT Computational Biology Group

Thu, 18 Dec 2014 14:47:01 -0600

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/

ShRec3D

Jitendra Narayan — Thu, 25 Dec 2014 23:14:52 -0600

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