BOL: Related items

Your stress/depression came from ancestor

Rahul Agarwal — Sun, 04 May 2014 18:46:20 -0500

"A study published in Nature Neuroscience finds that stress in early life alters the production of small RNAs, called microRNAs, in the sperm of mice. The mice show depressive behaviours that persist in their progeny."

Source:

http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049

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

Alignment-free sequence comparison tools available for next-generation sequencing data analysis

Abhimanyu Singh — Tue, 07 Nov 2017 05:33:33 -0600

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

https://github.com/bioinfo-ut/GenomeTester4/

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

http://clark.cs.ucr.edu/

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

Which of the followings are the best place to study Bioinformatics ?

Reshma Khatun — Sun, 28 Dec 2014 00:20:30 -0600

Bioinformatics is a major growth area and qualified Bioinformaticians are in high demand. An explosion in biological data has resulted from genome projects, next generation sequencing and other 'omics' techniques. Bioinformatics provides the tools to analyse and exploit such data sets.

Can you please suggest me the best place to study bioinformatics ( Grad/PostGrad).

Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.

Jit — Tue, 26 Dec 2017 22:23:39 -0600

Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

Roche has acquired Bina Technologies !!!

Jit — Tue, 30 Dec 2014 09:42:16 -0600

Bina Technologies is a privately held company that provides a big data platform for centralized management and processing of next generation sequencing (NGS) data for the academic and translational research markets. Bina will be integrated into the Roche Sequencing Unit, and will continue to focus on development of their innovative genomic analysis solution.

Roche has acquired Bina Technologies, a privately-owned biotech company based in California. The biotech’s first product was the Bina Box, a platform for secondary genomic analysis, sequence alignment, and variant calling, but since 2012, it has developed other products and platforms.

It is our shared vision with Roche that informatics and data sciences are critical elements of an end-to-end genomics solution. Fast, easy-to-use, scalable, and robust informatics solutions make a big difference in the quality and impact of the work of scientists and researchers. We believe in the future of data-driven, personalized medicine. We are passionate about accelerating that future together with Roche.

Financial details of the deal were not disclosed. For Roche, the move is yet another in a string of acquisitions. Last week (December 18), Roche paid $489 million for antibody maker Dutalys. And earlier this month, Roche bought prenatal testing company Ariosa Diagnostics.

Reference

http://blog.bina.com/news/bina-technologies-acquired-by-roche?&__hssc=109677338.1.1419953400266&__hstc=109677338.b8350f2729889b08f1325906d5236cd3.1419953400266.1419953400266.1419953400266.1&hsCtaTracking=96cac941-9372-4bbf-bacb-3ca6f1ff8cfd|3fce0f18-835b-4086-9345-388880861732

http://www.the-scientist.com/?articles.view/articleNo/41750/title/Roche-Buys-Bioinformatics-Firm/

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

Type Hinting

Pranjali Yadav — Fri, 09 Jan 2015 22:26:13 -0600

Python creator Guido van Rossum’s proposal for static type-checking annotations is inching closer to reality, and the feature has taken on a new name: type hinting.

Back in August, van Rossum published a proposal on the Python mailing list recommending type-checking annotations as a valuable feature for the next version of Python to improve the performance of editors and IDEs, linter capabilities, standard notation, and refactoring. Van Rossum’s latest proposal, posted late last month, outlined plans to publish a Python Enhancement Proposal (PEP) in early January to put the feature now known as type hinting on track for inclusion in Python 3.5, slated for release this September.

Reference

https://quip.com/r69HA9GhGa7J

Postdoctoral Researcher in Cancer Systems Biology

Mon, 12 Jan 2015 01:44:11 -0600

Postdoctoral Researcher in Cancer Systems Biology
Department of Oncology, Old Road Campus Research Building, Roosevelt Drive, Oxford
Grade 7: £30,434 - £37,394 with a discretionary range to £40,847 p.a.
Applications are invited for a Postdoctoral Researcher in Cancer Systems Biology to join a rapidly developing Bioinformatics Research Core group headed by Dr Anastasia Samsonova. The purpose of the role is to develop and deliver integrative approaches to dissect the complexity of cancer as a genomic disease. The research will focus on development and application of effective strategies for mining and integration of complex human *omics datasets and clinical/phenotypic data in cancer studies.

The role sits at the critical interface between genetics and cancer systems biology, and would suit a candidate who is interested in developing a career at the confluence of Statistics/Data Mining/Machine Learning and Biology. Ideally, you will have experience in development of analytical approaches to high-throughput and multivariate data mining and integration gained through a PhD (or equivalent) in a quantitative subject (eg mathematics, statistics, physics, engineering or computer science).

Experience of statistics and/or machine learning techniques is highly desirable as is evidence of prior experience of developing bioinformatics software and/or analysing complex *omics data sets. You will be able to work as part of a team and independently and deliver results to the required standard and schedule. You should be able to organise and prioritise your own work, as well as have excellent communication skills, both written and oral. The post will involve interactions with collaborators from such diverse fields as applied mathematics, statistics, computer science and medicine.

This is a full-time post, fixed-term until 31 March 2017. For informal enquiries, contact Dr Anastasia Samsonova (bioinformatics@oncology.ox.ac.uk).

All applicants must complete a short application form and upload a CV and supporting statement.

The closing date for applications is 12.00 noon on 26 January 2015.

More at https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.display_form

Indexcov: fast coverage quality control for whole-genome sequencing

Jit — Wed, 29 Aug 2018 09:20:46 -0500

indexcov, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large-scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample. Indexcov is available at https://github.com/brentp/goleft under the MIT license.

Address of the bookmark: https://github.com/brentp/goleft