BOL: Related items

ART: Set of Simulation Tools

Jit — Thu, 03 Nov 2016 08:28:25 -0500

ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the 1000 Genomes Project . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. VarSim) for evaluating variant calling tools or methods.

Address of the bookmark: http://www.niehs.nih.gov/research/resources/software/biostatistics/art/

QuorUM: An Error Corrector for Illumina Reads

Jit — Wed, 08 Nov 2017 11:40:41 -0600

Illumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term “error correction” to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.

QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821

Common steps for reads mapping !

BioStar — Thu, 09 Mar 2023 02:48:02 -0600

Mapping reads to a reference genome is an essential step in many types of genomic analysis, such as variant calling and gene expression analysis. Here are some general steps to follow for mapping reads to a genome:

Choose a read mapper: There are many read mappers available, such as BWA, Bowtie, and HISAT2. Choose a mapper that is appropriate for your type of data and research question.
Index the reference genome: Before mapping reads, the reference genome needs to be indexed. This involves creating an index of the genome sequence that allows the mapper to quickly find matches to the reads. Most mappers have their own indexing tools.
Prepare the read data: The reads should be in a format that is compatible with the mapper. Most mappers accept FASTQ or BAM files. Depending on the quality of the data, it may need to be filtered or trimmed before mapping.
Run the mapper: The mapper is run with the command-line interface or using a graphical user interface. The specific command depends on the mapper being used, but typically involves specifying the input data, reference genome, and output file format.
Evaluate the mapping results: After the mapping is complete, the results should be evaluated. This includes assessing the quality of the mapping, such as the mapping rate, the number of mapped reads, and the mapping quality score.
Post-processing: Depending on the analysis being performed, post-processing of the mapped reads may be necessary. This can include filtering reads based on quality, removing duplicate reads, and calling variants.

Overall, mapping reads to a reference genome is a complex process that requires careful consideration of the type of data, the research question, and the specific mapper being used.

World of Omics

Rahul Agarwal — Tue, 16 Jul 2013 17:11:48 -0500

How many variants of "omics" techniques presently in use ?

Does anyone have Nanopore latest updates?

Poonam Mahapatra — Mon, 12 Aug 2013 12:19:29 -0500

There was a lot of buzz about Oxford Nanopore Technologies® is developing the GridION™ system and miniaturised MinION™ device. These are a new generation of electronic molecular analysis system for use in scientific research, personalised medicine, crop science, security/defence and more. The platform technology uses nanopores to analyse single molecules including DNA/RNA and proteins. With a broad patent portfolio, the Oxford Nanopore pipeline includes biological nanopores and solid-state nanopores.

Is this available, or still under trial mode?

https://www.nanoporetech.com/

https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system

Two major breakthrough!!

Rahul Agarwal — Mon, 02 Sep 2013 10:18:11 -0500

"Scientists in Uruguay in colloboration with European partners sequenced the genome of the high-value Tannat grape, from which "the most healthy of red wines" are fermented.

A quick, $1 syphilis test in development by researchers from UNU-BIOLAC."

Source:

http://www.sciencedaily.com/releases/2013/09/130902101846.htm

http://www.eurekalert.org/pub_releases/2013-09/tca-ssg082613.php

Encode sequencing data freely available to download and use for academic means

Rahul Agarwal — Thu, 13 Mar 2014 18:18:08 -0500

In Encode, regulatory elements investigated via DNA hypersensitivity assays, assays of DNA methylation, and chromatin immunoprecipitation (ChIP) of proteins that interact with DNA, including modified histones and transcription factors, followed by sequencing (ChIP-Seq).

More information:

https://genome.ucsc.edu/ENCODE/pilot.html

Address of the bookmark: https://genome.ucsc.edu/ENCODE/

Tsetse Fly Genome sequenced

Rahul Agarwal — Fri, 25 Apr 2014 10:48:35 -0500

As it reported online today in Science, the team used several sequencing approaches to tackle the tsetse fly's 366 million base genome.

The current study, and companion articles slated to appear in PLOS One, PLOS Genetics, and PLOS Neglected Tropic Diseases, are the result of nearly 150 researchers based in 18 countries.

Source:

http://www.genomeweb.com/sequencing/international-team-sequences-tsetse-fly-genome

Science for Life Laboratory (SciLifeLab)-Sweden

Sat, 10 May 2014 06:22:30 -0500

Science for Life Laboratory (SciLifeLab) is a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a national resource and a collaboration between four universities: Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University and Uppsala University.

Webpage : https://www.scilifelab.se/about-us/
Opportunity: https://www.scilifelab.se/about-us/career/

Genomics and sequencing approach for identification of biomarkers to assess the efficacy of TGF-βRI inhibitors (of liver cancer) in vivo

Rahul Agarwal — Tue, 05 Aug 2014 13:55:32 -0500

Liver cancer is third leading cause of deaths and fourth most frequent occuring cancer worldwide. There are multiple signaling pathways responsible for causing cancer amongst which TGFb is most important cytokine whose signaling pathway promote cancer. However, main problem is to cure this cancer at late stage where we still have no treatment strategy to tackle this deadly cancer. Hence we need to find out new therapeutic target. One way is to look the relationships between mRNA, methylation and miRNA data of patients with different pathological conditions (cancer vs control either with inhibitor/not). MiRNA is small RNA molecules known to inhibit mRNA expression of particular gene by binding improperly to 3'UTR region of a gene and hence block binding of TF /translation of gene. CpG regions is known to located at promoter region of gene (5' UTR) and usually hypomethylated which allow to gene to transcribe and translate however sometime this region become hyper-methylated thats prevent expression of host gene. Thus , integration of these three data reveal new targets and pathways important for causing or preventing cancer and also reveal biomarker thats check the effects of inhibitor on signaling pathway underlying liver cancer.