<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43650? https://bioinformaticsonline.com/bookmarks/view/43583/pango-lineage-analysis Mon, 15 Nov 2021 03:38:29 -0600 https://bioinformaticsonline.com/bookmarks/view/43583/pango-lineage-analysis <![CDATA[Pango Lineage Analysis !]]> The Pango nomenclature is being used by researchers and public health agencies worldwide to track the transmission and spread of SARS-CoV-2, including variants of concern. This website documents all current Pango lineages and their spread, as well as various software tools which can be used by researchers to perform analyses on SARS-COV-2 sequence data.

Address of the bookmark: https://cov-lineages.org/resources/pangolin/output.html

]]> Abhi https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant Fri, 26 Nov 2021 12:53:12 -0600 https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant <![CDATA[Classification of SARS-CoV2 Variant !]]> The scientists established some guidelines for determining whether a variant is a legitimate branch of an existing lineage:

The variant should be transmitted from its original location to another "geographically distinct population"—say, another country or a province of a large and populous country.
It should differ from its ancestor by at least one nucleotide.
At least 95% of its genetic code should have been sequenced at least five times from different samples.

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43552/understanding-pango-networks Sat, 16 Oct 2021 14:02:36 -0500 https://bioinformaticsonline.com/bookmarks/view/43552/understanding-pango-networks <![CDATA[Understanding pango networks !]]> In the vast majority of instances it is expected that Pango lineage names and designations will conform to the following rules. These rules also act as guidelines for the decisions made by the Lineage Designation Committee.

https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

https://www.pango.network/how-does-the-system-work/what-are-pango-lineages/

Reference paper

https://www.nature.com/articles/s41564-020-0770-5

Address of the bookmark: https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/41493/coronavirus-resources Wed, 25 Mar 2020 17:11:33 -0500 https://bioinformaticsonline.com/bookmarks/view/41493/coronavirus-resources <![CDATA[Coronavirus Resources !]]> 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse 

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data 

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

]]> Neel https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments Mon, 27 Jun 2016 11:01:44 -0500 https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments <![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]> Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

Krona

https://sourceforge.net/p/krona/home/krona/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing Thu, 09 Aug 2018 04:09:37 -0500 https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing <![CDATA[PureCN: copy number calling and SNV classification using targeted short read sequencing]]> This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.

Author: Markus Riester [aut, cre], Angad P. Singh [aut]

Maintainer: Markus Riester <markus.riester at novartis.com>

Citation (from within R, enter citation("PureCN")):

Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). “PureCN: Copy number calling and SNV classification using targeted short read sequencing.” Source Code for Biology and Medicine, 11, 13. doi: 10.1186/s13029-016-0060-z.

Address of the bookmark: http://bioconductor.org/packages/release/bioc/html/PureCN.html

]]> Jit https://bioinformaticsonline.com/pages/view/44236/type-of-ssr Thu, 09 Mar 2023 04:35:41 -0600 https://bioinformaticsonline.com/pages/view/44236/type-of-ssr <![CDATA[Type of SSR]]>

Types of SSRs (simple sequence repeats), SSRs are short DNA sequences consisting of a tandem repeat of a few nucleotides, typically 2-6 nucleotides in length. There are different types of SSRs based on the length and pattern of the repeated sequence, as well as the presence or absence of interruptions of non-repeated nucleotides within the repeat array. The four types of SSRs are:

  1. Perfect SSR: This is the simplest type of SSR, where the same repeat motif is present adjacent to each other without any interruption of any other nucleotide. For example, a perfect SSR with the repeat motif "CAT" would be "CATCATCATCAT", where the "CAT" sequence is repeated four times.

  2. Imperfect SSR: This type of SSR contains repeat motifs that are interrupted by one or a few non-repeat nucleotides. For example, an imperfect SSR with the repeat motif "CAT" would be "CATCATGGCATCATCAT", where the "CAT" sequence is repeated twice, but interrupted by "GG".

  3. Compound perfect SSR: This type of SSR contains two or more repeat motifs lying adjacent to each other, separated by no or very few intervening nucleotides. For example, a compound perfect SSR with the repeat motifs "CAT" and "GTC" would be "CATCATCATGTCGTC", where the "CAT" sequence is repeated three times, followed by the "GTC" sequence repeated twice.

  4. Compound imperfect SSR: This type of SSR contains two or more repeat motifs interrupted by several non-repeat nucleotides. For example, a compound imperfect SSR with the repeat motifs "CAT" and "GTC" would be "CATCATCATNNNNNNNGTCGTCGTC", where the "CAT" sequence is repeated three times, interrupted by several non-repeat nucleotides, followed by the "GTC" sequence repeated three times.

]]> BioStar https://bioinformaticsonline.com/blog/view/41455/coronavirus-covid-%E2%80%9019-testing-sites-in-india Mon, 16 Mar 2020 16:13:41 -0500 https://bioinformaticsonline.com/blog/view/41455/coronavirus-covid-%E2%80%9019-testing-sites-in-india <![CDATA[Coronavirus COVID ‐19 Testing Sites In India]]> COVID-19 is a new illness that can affect your lungs and airways. It's caused by a virus called coronavirus.

Stay at home if you have coronavirus symptoms

Stay at home if you have either:

  • a high temperature – you feel hot to touch on your chest or back
  • a new, continuous cough – this means you've started coughing repeatedly

DO NOT TAKE

Ibrufen

https://amp.theguardian.com/world/2020/mar/14/anti-inflammatory-drugs-may-aggravate-coronavirus-infection

How to avoid catching and spreading coronavirus (social distancing)

Everyone should do what they can to stop coronavirus spreading.

It is particularly important for people who:

  • are 70 or over
  • have a long-term condition
  • are pregnant
  • have a weakened immune system

image

Below are the 52 Coronavirus COVID-19 Testing sites/locations in India.

State: Andhra Pradesh       

  1. Sri Venkateswara Institute of Medical Sciences, Tirupati
  2. Andhra Medical College, Visakhapatnam, Andhra Pradesh
  3. GMC, Anantapur, AP

State: Andaman & Nicobar islands

  1. Regional Medical Research Centre, Port Blair, Andaman, and Nicobar

State: Assam

  1. Gauhati Medical College, Guwahati
  2.  Regional Medical Research Center, Dibrugarh

State: Bihar

  1. Rajendra Memorial Research Institute of Medical Sciences, Patna

State: Chandigarh

  1. Post Graduate Institute of Medical Education & Research, Chandigarh

State: Chhattisgarh

  1. All India Institute Medical Sciences, Raipur

Union Territory: Delhi-NCT 

  1. All India Institute Medical Sciences, Delhi
  2. National Centre for Disease Control, Delhi

State: Gujarat

  1. BJ Medical College, Ahmedabad
  2. M.P.Shah Government Medical College, Jamnagar

State: Haryana

  1. Pt. B.D. Sharma Post Graduate Inst. of Med. Sciences, Rohtak, Haryana
  2. BPS Govt Medical College, Sonipat

State: Himachal Pradesh

  1. Indira Gandhi Medical College, Shimla, Himachal Pradesh
  2. Dr.Rajendra Prasad Govt. Med. College, Kangra, Tanda, HP

Union Territory: Jammu and Kashmir

  1. Sher‐e‐ Kashmir Institute of Medical Sciences, Srinagar
  2. Government Medical College, Jammu

State: Jharkhand

  1. MGM Medical College, Jamshedpur

State: Karnataka

  1. Bangalore Medical College & Research Institute, Bangalore
  2. National Institute of Virology Field Unit Bangalore
  3. Mysore Medical College & Research Institute, Mysore
  4. Hassan Inst. of Med. Sciences, Hassan, Karnataka
  5. Shimoga Inst. of Med. Sciences, Shivamogga, Karnataka

State: Kerala

  1. National Institute of Virology Field Unit, Kerala
  2. Govt. Medical College, Thiruvananthapuram, Kerala
  3. Govt. Medical College, Kozhikode, Kerala

State: Madhya Pradesh

  1. All India Institute Medical Sciences, Bhopal
  2. National Institute of Research in Tribal Health (NIRTH), Jabalpur

State: Meghalaya

  1. NEIGRI of Health and Medical Sciences, Shillong, Meghalaya

State: Maharashtra

  1. Indira Gandhi Government Medical College, Nagpur
  2. Kasturba Hospital for Infectious Diseases, Mumbai

State: Manipur

  1. J N Inst. of Med. Sciences Hospital, Imphal‐East, Manipur

State: Odisha

  1. Regional Medical Research Center, Bhubaneswar

Union Territory: Puducherry

  1. Jawaharlal Institute of Postgraduate Medical Education & Research, Puducherry

State: Punjab

  1. Government Medical College, Patiala, Punjab
  2. Government Medical College, Amritsar

State: Rajasthan

  1. Sawai Man Singh, Jaipur
  2. Dr. S.N Medical College, Jodhpur
  3. Jhalawar Medical College, Jhalawar, Rajasthan
  4. SP Med. College, Bikaner, Rajasthan

State: Tamil Nadu

  1. King’s Institute of Preventive Medicine & Research, Chennai
  2. Government Medical College, Theni

State: Tripura

  1. Government Medical College, Agartala

State: Telangana

  1. Gandhi Medical College, Secunderabad

State: Uttar Pradesh

  1. King’s George Medical University, Lucknow
  2. Institute of Medical Sciences, Banaras, Hindu University, Varanasi
  3. Jawaharlal Nehru Medical College, Aligarh

State: Uttarakhand

  1. Government Medical College, Haldwani

State: West Bengal

  1. National Institute of Cholera and Enteric Diseases, Kolkata
  2. IPGMER, Kolkata
]]> Neel https://bioinformaticsonline.com/bookmarks/view/43972/covid-spectrum Sun, 25 Sep 2022 18:58:14 -0500 https://bioinformaticsonline.com/bookmarks/view/43972/covid-spectrum <![CDATA[Covid Spectrum !]]> https://cov-spectrum.org/explore/World/AllSamples/

CoV-Spectrum supports a wide range of search queries. The following sections presents search options of the basic search bar. For information on the advanced search

Address of the bookmark: https://cov-spectrum.org/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/28121/kaiju Mon, 27 Jun 2016 11:23:04 -0500 https://bioinformaticsonline.com/bookmarks/view/28121/kaiju <![CDATA[Kaiju]]> Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/

]]> Jit