BOL: Related items

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/

A History of Bioinformatics (in the Year 2039)

Wed, 23 Jul 2014 06:37:51 -0500

C. Titus Brown http://video.open-bio.org/video/1/a-history-of-bioinformatics-in-the-year-2039

PLAST: A fast, accurate and NGS scalable bank-to-bank sequence similarity search tool

Jit — Fri, 01 Dec 2017 04:10:54 -0600

PLAST is a fast, accurate and NGS scalable bank-to-bank sequence similarity search tool providing significant accelerations of seeds-based heuristic comparison methods, such as the Blast suite of algorithms.

Relying on unique software architecture, PLAST takes full advantage of recent multi-core personal computers without requiring any additional hardware devices.

PLAST stands for Parallel Local Sequence Alignment Search Tool and is was published in BMC Bioinformatics.

PLAST is a general purpose sequence comparison tool providing the following benefits:

PLAST is a high-performance sequence comparison tool designed to compare two sets of sequences (query vs. reference),
Reduces the processing time of sequences comparisons while providing highest quality results,
Contains a fully integrated data filtering engine capable of selecting relevant hits with user-defined criteria (E-Value, identity, coverage, alignment length, etc.),
Does not require any additional hardware, since it is a software solution. It is easy to install, cost-effective, takes full advantage of multi-core processors and uses a small RAM footprint,
Ready to be used on desktop computer, cluster, cloud as well as within distributed system running Hadoop.

https://plast.inria.fr/

Address of the bookmark: https://plast.inria.fr/

Scientists map 17,294 proteins produced in human body

Jit — Thu, 29 May 2014 01:57:55 -0500

Indian scientists missed the genomic profiling bus, but they've more than made up for it by creating the first human proteome map which is an extension of the genomic study. Till now, here is no direct equivalent for the human proteome. But recently two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome.

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB

MIX: Combining multiple assemblies from NGS data

Rahul Nayak — Tue, 08 May 2018 04:58:05 -0500

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

HALC: High throughput algorithm for long read error correction

Jit — Fri, 08 Jun 2018 10:47:41 -0500

HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region’s repeats in the contigs sufficiently similar to it (similar repeat based alignment approach) HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms.

Address of the bookmark: https://github.com/lanl001/halc

Genomics and Personalized Medicine

Sun, 01 Jun 2014 23:38:42 -0500

(October 20, 2009) Michael Snyder, Professor of Genetics and Chair of the Department of Genetics at Stanford, discusses advances in gene sequencing, the impact of genomics on medicine, the potential for personalized medicine. and efforts at Stanford to further study these issues. Stanford Mini Med School is a series arranged and directed by Stanford's School of Medicine, and presented by the Stanford Continuing Studies program. Featuring more than thirty distinguished, faculty, scientists and physicians from Stanford's medical school, the series offers students a dynamic introduction to the world of human biology, health and disease, and the groundbreaking changes taking place in medical research and health care. Stanford University http://www.stanford.edu Stanford University School of Medicine http://med.stanford.edu Stanford Continuing Studies http://continuingstudies.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

Jit — Fri, 06 Jul 2018 03:36:45 -0500

KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:

hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
- kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
- seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
- density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
- profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
- spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
- spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
- spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339

Address of the bookmark: https://github.com/TGAC/KAT

Adhoc Bioinformatics Faculty Position @ NIT

Tue, 03 Jun 2014 16:19:52 -0500

NATIONAL INSTITUTE OF TECHNOLOGY, DEPARTMENT OF BIOTECHNOLOGY, WARANGAL – 506 021, Andhra Pradesh

No.NITW/BT/2014/adhoc

APPLICATIONS ARE INVITED FOR THE APPOINTMENT OF ADHOC FACULTY ON CONTRACT BASIS IN THE DEAPARTMENT OF BIOTECHNOLOGY

Period of Contract: Initially the appointment is for one semester i.e., from July 2014 up to December 2014 only.

Essential Qualifications:

i) B. Tech or equivalent in Biotechnology/ Industrial Biotechnology/ Biochemical Engineering / Chemical Engg. Or M. Sc in Microbiology/ Botany/ Zoology/ Biochemistry/Biotechnology and ii) M. Tech or equivalent in Biotechnology/Industrial Biotechnology/Bioinformatics

Integrated M. Tech in Biotechnology/Industrial Biotechnology/ Bioinformatics

Candidates must possess First class (60% aggregate marks or 6.5 CGPA) at B. Tech/ M. Sc and M. Tech.

Desirable: Ph. D Pay Package: All selected candidates shall be eligible for a consolidated pay of Rs.30, 000/- per month. Candidates with Ph. D shall be eligible for an additional amount of Rs.5, 000/- per month.

How to apply : Applications on plain paper with attested photocopies of certificate and bio data along with justification for eligibility should reach to the Head, Department of Biotechnology, National Institute of Technology, Warangal AP 506004 in the form of soft or hard copy on or before 21st June 2014 email : biotech_hod@nitw.ac.in

Intimation: No separate call letters will be sent to the candidates. All the eligible candidates will be notified in the institute web site on 23rd June 2014. All the eligible candidates are requested to report for the interview to the Head, Department of Biotechnology at 9:00 AM on 27th June 2014

Joining: Selected candidates will be informed and they are expected to join immediately.

http://www.nitw.ac.in/nitw/announcements/2014/Bio-Adhoc%20Advt.%20May-2014.pdf

AlignQC: A tool for assessing an alignment, and generating reports that are easy to share

Jit — Tue, 07 Aug 2018 04:41:07 -0500

Long read alignment analysis. Generate a reports on sequence alignments for mappability vs read sizes, error patterns, annotations and rarefraction curve analysis. The most basic analysis only requires a BAM file, and outputs a web browser compatible xhtml to visualize/share/store/extract analysis results.

https://f1000research.com/articles/6-100/

https://github.com/jason-weirather/AlignQC

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/AlignQC/