BOL: Related items

Bioinformatics algorithms tutorials

John Parker — Tue, 24 Jun 2014 00:10:45 -0500

Useful bioinformatics tutorial, such as

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

fragScaff: Genome Assembly with Contiguity Preserving Transposition

Jit — Mon, 14 May 2018 04:28:14 -0500

Contiguity preserving transposition and sequencing (CPT-seq) is an entirely in vitro means of generating libraries comprised of 9216 indexed pools, each of which contains thousands of sparsely sequenced long fragments ranging from 5 kilobases to >1 megabase. This software, fragScaff, leverages coincidences between the content of different pools as a source of contiguity information for scaffolding de novo genome assemblies. FragScaff is complementary to Lachesis, providing midrange contiguity to support robust, accurate chromosome-scale de novo genome assemblies without the need for laborious in vivo cloning steps.

Further information about fragScaff, including source code, is available at:https://sourceforge.net/projects/fragscaff/files.

Manuscript describing fragScaff was published as: Adey A, Kitzman JO, Burton JN, Daza R, Kumar A, Christiansen L, Ronaghi M, Amini S, L Gunderson K, Steemers FJ, Shendure J#. In vitro, long-range sequence information for de novo genome assembly via transposase contiguity. Genome Research 2014 Dec;24(12):2041-9. doi: 10.1101/gr.178319.114. PubMed PMID: 25327137.

Address of the bookmark: https://sourceforge.net/projects/fragscaff/files/

Orione – a web-based framework for NGS analysis in microbiology

Martin Jones — Wed, 23 Jul 2014 06:43:03 -0500

End-to-end NGS microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult due to a lack of interoperability, reproducibility, and transparency. To overcome these limitations researchers at CRS4, Italy have developed Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for NGS microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.

Reference

Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014) Orione, a web-based framework for NGS analysis in microbiology. Bioinformatics [Epub ahead of print]. [article]

Address of the bookmark: http://orione.crs4.it/

You can't hide from Genome Hackers

Neel — Sat, 13 Oct 2018 14:17:28 -0500

Young computational biologist named Yaniv Erlich shocked the research world by showing it was possible to unmask the identities of people listed in anonymous genetic databases using only an Internet connection

Paper: http://science.sciencemag.org/content/early/2018/10/10/science.aau4832

More at https://www.wired.com/story/genome-hackers-show-no-ones-dna-is-anonymous-anymore/

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

Finishing !!

Jit — Sat, 20 May 2017 15:50:20 -0500

The process of finishing a genome and moving it from a draft stage (the result of sequencing and initial assembly) to a complete genome is typically a time and resource intensive task. The advent of new sequencing technologies has come with its own set of opportunities and pitfalls in the finishing process. While genomes can now be sequenced to high redundancy in a cost-effective manner, the process of assembling the genomes is more challenging and often draft genomes are fragmented into hundreds of contigs. Correspondingly, the task of producing the complete genome can involve months of lab work and thousands of finishing experiments and is usually done in large genome centers.

The work in our lab has focussed on computational approaches to speed-up the finishing process. Specifically, we have explored the use of optical mapping and mate-pair data to augment assemblies and direct finishing experiments. The tools developed in our lab have been used in several finishing projects, producing complete genomes (and near-complete ones) with surprisingly little computational and experimental effort (Nagarajan et al., in submission). The executables (as well as source code) for these tools are freely available here:

Scaffolding using Optical Restriction Mapping
Optical Maps are global, ordered maps of restriction site locations in a genome. This information can be quite useful in scaffolding contigs from a shotgun assembly to guide the finishing process. A set of programs to exploit optical maps for assembly can be found here: SOMA v2.0 (63 MB tar.gz file). This version of SOMA contains several improvements to programs in v1.0 as well as new scripts for working with multiple maps, contig graphs and scaffolds.
Augmenting assemblies with mate-pair data
Mate-pair information can be valuable in augmenting short-read assemblies and reconstructing the genome as larger scaffolds. AMOS-Hybrid is a pipeline written in the AMOS framework (open-source assembly tools) to merge arbitrary mated reads into an existing assembly and merge contigs and create scaffolds where possible. Source code and executables for AMOS-Hybrid are available here: AMOS-Hybrid v1.0 (142 MB tar.gz file).
Assembly and sequence-composition guided finishing
Contigs from a shotgun assembly are typically linked together in a graph structure that can serve to guide finishing and in some case close gaps in-silico. Also, in many cases, sequence composition of contigs can provide clues to fill gaps in scaffolds. A set of scripts to automate some of these tasks can be found here: Finishing Scripts v1.0 (63 MB tar.gz file).

http://www.cbcb.umd.edu/finishing/

Address of the bookmark: http://www.cbcb.umd.edu/finishing/

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