<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/22807?offset=1140 https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite Wed, 06 Oct 2021 07:01:14 -0500 https://bioinformaticsonline.com/bookmarks/view/43439/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite <![CDATA[MMseqs2: ultra fast and sensitive sequence search and clustering suite]]> MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Address of the bookmark: https://github.com/soedinglab/MMseqs2

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/44904/termal-a-fast-and-interactive-terminal-based-viewer-for-multiple-sequence-alignments Mon, 22 Sep 2025 23:51:02 -0500 https://bioinformaticsonline.com/bookmarks/view/44904/termal-a-fast-and-interactive-terminal-based-viewer-for-multiple-sequence-alignments <![CDATA[Termal: a fast and interactive terminal-based viewer for multiple sequence alignments]]> termal, a fast, interactive, terminal-based viewer for multiple sequence alignments (MSAs), designed for use on remote systems such as high-performance computing (HPC) clusters.

https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf208/8257678?login=true

Address of the bookmark: https://github.com/sib-swiss/termal

]]> LEGE https://bioinformaticsonline.com/news/view/4191/high-density-sheep-snp-genotyping-chip-released Tue, 03 Sep 2013 13:58:04 -0500 https://bioinformaticsonline.com/news/view/4191/high-density-sheep-snp-genotyping-chip-released <![CDATA[High Density Sheep SNP Genotyping Chip released!!!]]> If you are working on Sheep genomics then there is a good news for you. FarmIQ in conjunction with Illumina and the International Sheep Genomics Consortium (ISGC) are today announcing completion of the “Ovine Infinium® HD SNP BeadChip”, a high definition SNP chip for ship genome. The OvineSNP50 BeadChip features over 54,241 evenly spaced probes that target SNPs, offering more than sufficient SNP density for genome-wide association studies and other applications such as genome-wide selection, determination of genetic merit, identification of quantitative trait loci, and comparative genetic studies.

The BeadChip was developed in collaboration with leading ovine researchers from AgResearch, Baylor UCSC, CSIRO, and the USDA as part of the International Sheep Genomics Consortium. It features over 54,241 evenly spaced probes that target single nucleotide polymorphisms (SNPs). More than 18,000 of these markers were discovered through sequencing reduced representation libraries with the Illumina Genome Analyzer IIx. A set of 600 SNPs were identified by BAC end sequencing and validated with Illumina GoldenGate Genotyping Assays over 403 animals from 23 breeds. The remaining SNPs were derived from the draft ovine genome.

Read more @

http://res.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf

http://www.illumina.com/products/ovinesnp50_dna_analysis_kit.ilmn

]]> Rahul Agarwal https://bioinformaticsonline.com/bookmarks/view/37536/snippy-rapid-haploid-variant-calling-and-core-snp-phylogeny Sat, 11 Aug 2018 11:06:56 -0500 https://bioinformaticsonline.com/bookmarks/view/37536/snippy-rapid-haploid-variant-calling-and-core-snp-phylogeny <![CDATA[Snippy: Rapid haploid variant calling and core SNP phylogeny]]> Snippy finds SNPs between a haploid reference genome and your NGS sequence reads. It will find both substitutions (snps) and insertions/deletions (indels). It will use as many CPUs as you can give it on a single computer (tested to 64 cores). It is designed with speed in mind, and produces a consistent set of output files in a single folder. It can then take a set of Snippy results using the same reference and generate a core SNP alignment (and ultimately a phylogenomic tree).

snippy --cpus 16 --outdir mysnps --ref Listeria.gbk --R1 FDA_R1.fastq.gz --R2 FDA_R2.fastq.gz

Address of the bookmark: https://github.com/tseemann/snippy

]]> Neel https://bioinformaticsonline.com/blog/view/44865/snp-analysis-unlocking-the-secrets-in-our-dna Wed, 16 Jul 2025 01:31:45 -0500 https://bioinformaticsonline.com/blog/view/44865/snp-analysis-unlocking-the-secrets-in-our-dna <![CDATA[SNP Analysis: Unlocking the Secrets in Our DNA]]> Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation in humans—and many other organisms. A single base change in the DNA sequence (for example, an A instead of a G) can influence everything from our eye color to our risk of developing diseases. Analyzing these tiny changes has become central to modern genetics, medicine, agriculture, and evolutionary biology.

What are SNPs?
SNPs (pronounced "snips") are positions in the genome where individuals differ by a single nucleotide. For example:

Reference: ...A T G C A T G A...
Variant:     ...A T G T A T G A...

Here, the C in the reference genome has been replaced by a T in the variant.

SNPs occur roughly every 300–1,000 bases in the human genome, meaning there are millions of them scattered throughout our DNA. Most SNPs have no effect on health, but some are linked to disease susceptibility, drug response, and other traits.

Why Do We Analyze SNPs?
1. Medical Genetics

Identify disease-associated variants (e.g., BRCA1/2 in breast cancer).

Predict drug response (pharmacogenomics).

Enable precision medicine by tailoring treatments.

2. Population Genetics & Ancestry

Trace human migration and ancestry.

Study genetic diversity within and between populations.

3. Agriculture & Animal Breeding

Select for desirable traits (drought resistance, yield, disease resistance).

Improve breeding efficiency in livestock.

4. Evolutionary Biology

Track natural selection.

Study adaptation in wild populations.

How is SNP Analysis Performed?
SNP analysis can be broadly divided into three steps:

SNP Detection
Genotyping arrays: Chips that test hundreds of thousands of known SNP positions simultaneously. Fast and affordable, widely used in consumer ancestry testing.

Whole-genome or whole-exome sequencing: Can detect known and novel SNPs across the genome.

Targeted sequencing or PCR: For focused analysis of specific regions.

Variant Calling
Sequencing data is aligned to a reference genome. Bioinformatics tools (e.g., GATK, bcftools) identify positions where the sequenced sample differs from the reference.

Annotation and Interpretation
Tools (e.g., SnpEff, VEP) predict the functional impact of SNPs.

Are the SNPs in coding regions? Do they cause amino acid changes? Are they known to be pathogenic?

Databases like dbSNP, ClinVar, and GWAS Catalog provide information on known associations.

Common Tools for SNP Analysis
Alignment: BWA, Bowtie2

Variant Calling: GATK, FreeBayes

Visualization: IGV, UCSC Genome Browser

Annotation: SnpEff, VEP

Statistical Analysis: PLINK, SNPTEST

Challenges in SNP Analysis
False positives/negatives: Sequencing errors, alignment issues.

Population stratification: Confounding in association studies.

Interpretation: Many SNPs have unknown or complex effects.

Researchers address these with rigorous quality control, large datasets, and increasingly sophisticated statistical models.

The Future of SNP Analysis
With advances in sequencing technology and AI-driven analysis, SNP studies are expanding:

Polygenic risk scores predict disease risk based on thousands of SNPs.

Large-scale biobanks (e.g., UK Biobank, All of Us) enable powerful genome-wide association studies (GWAS).

CRISPR and functional assays help validate SNP effects in the lab.

SNP analysis is at the heart of the genomic revolution, promising insights into biology, health, and evolution at unprecedented scale.

Conclusion
From diagnosing rare diseases to designing better crops, SNP analysis is a foundational tool in modern science. As our ability to sequence and interpret genomes improves, so will our understanding of these tiny—but mighty—variations in DNA.

 

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/40705/malva-genotyping-by-mapping-free-allele-detection-of-known-variants Tue, 28 Jan 2020 03:39:22 -0600 https://bioinformaticsonline.com/bookmarks/view/40705/malva-genotyping-by-mapping-free-allele-detection-of-known-variants <![CDATA[MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants]]> MALVA is able to genotype multi-allelic SNPs and indels without mapping reads

MALVA calls correctly more indels than the most widely adopted genotyping pipelines

Mapping-free approaches are as accurate as alignment-based ones, while being faster

More at https://www.sciencedirect.com/science/article/pii/S2589004219302366

https://www.sciencedirect.com/science/article/pii/S2589004219302366

Address of the bookmark: https://github.com/AlgoLab/malva

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data Tue, 28 Nov 2017 06:49:11 -0600 https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data <![CDATA[simNGS and simLibrary – Software for Simulating Next-Gen Sequencing Data]]> simNGS is software for simulating observations from Illumina sequencing machines using the statistical models behind the AYB base-calling software. By default, observations only incorporate noise due to sequencing and do not incorporate effects from more esoteric sources of noise that may be present in real data ("dust", bubbles, merged clusters, sequence-heterogeneous clusters, etc). Many of these additional sources may optionally applied.

simNGS takes fasta format sequences and a file describing the covariance of noise between bases and cycles observed in an actual run of the machine, randomly generates noisy intensities representing the signals for the sequence at each cycle and calculates likelihoods for all possible base calls.

Address of the bookmark: https://www.ebi.ac.uk/goldman-srv/simNGS/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software Tue, 08 May 2018 04:39:39 -0500 https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software <![CDATA[EvidentialGene: tr2aacds, mRNA Transcript Assembly Software]]> EvidentialGene is a genome informatics project, "Evidence Directed Gene Construction for Eukaryotes", to construct high quality, accurate gene sets for animals and plants, developed by Don Gilbert at Indiana University, see
http://arthropods.eugenes.org/EvidentialGene/

Construction refers to the combination of classical gene prediction, and more recent gene assembly (de-novo and genome-assisted) methods. The basic Evigene methods involve using available best-of-breed gene prediction and assembly software, combining all evidence for genes, from expressed sequences, genome assembly sequences, related species protein sequences, and any other, to annotate and score gene constructions. Over-produced constructions are classified by gene evidence for best qualities per "locus", including genome-aligned and gene-transcript aligned (genome-free) locus identification. All software developed for EvidentialGene is publicly available. See project wiki/blog for notes.

Download 

http://arthropods.eugenes.org/EvidentialGene/trassembly.html

https://sourceforge.net/p/evidentialgene/blog/

Address of the bookmark: http://arthropods.eugenes.org/EvidentialGene/trassembly.html

]]> Rahul Nayak https://bioinformaticsonline.com/opportunity/view/24464/guest-faculty-job-vacancies-in-pondicherry-university Tue, 22 Sep 2015 23:50:16 -0500 <![CDATA[Guest Faculty Job vacancies in Pondicherry University]]> Guest Faculty Job vacancies in Pondicherry University
Qualification : M.Phil. / M.Tech. / M.Sc. in Computer Science / Master of Computer Applications with a minimum of 55% of marks. Candidates with Ph.D. degree and NET/SLET qualification will be given preference as per UGC norms.

Desirable : Research or teaching experience in Bioinformatics and Computational Biology.
Honorarium : Rs. 1,000/- per lecture (subject to a maximum of 25,000/- per month)
How to apply

Interested eligible candidates may attend the 'walk-in' interview along with all original certificates and testimonials with a copy of their bio-data. Walk-in-interview will be held on 28.09.2015 (Monday), 03:00 P.M. at the office of the Dean, School of Life Sciences, Science Block — I, Pondicherry University, Puducherry — 605 014. Candidates reporting after 03:00 P.M. will not be entertained.

More at http://www.pondiuni.edu.in/news/walk-interview-guest-faculty-centre-bioinformatics

]]> https://bioinformaticsonline.com/pages/view/33837/awesome-perl-frameworks-libraries-and-software-part-1 Fri, 07 Jul 2017 04:06:59 -0500 https://bioinformaticsonline.com/pages/view/33837/awesome-perl-frameworks-libraries-and-software-part-1 <![CDATA[Awesome perl frameworks, libraries and software - PART 1]]> A curated list of awesome Perl frameworks, libraries and software.

]]> Neel