<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/19980?offset=1510 https://bioinformaticsonline.com/bookmarks/view/39372/irnad-a-computational-tool-for-identifying-d-modification-sites-in-rna-sequence Thu, 16 May 2019 00:20:07 -0500 https://bioinformaticsonline.com/bookmarks/view/39372/irnad-a-computational-tool-for-identifying-d-modification-sites-in-rna-sequence <![CDATA[iRNAD: a computational tool for identifying D modification sites in RNA sequence]]> iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. 

http://lin-group.cn/server/iRNAD/

Address of the bookmark: http://lin-group.cn/server/iRNAD/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/39881/apollo-a-sequence-annotation-editor Tue, 27 Aug 2019 08:08:47 -0500 https://bioinformaticsonline.com/bookmarks/view/39881/apollo-a-sequence-annotation-editor <![CDATA[Apollo: a sequence annotation editor]]> The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them

Address of the bookmark: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-12-research0082

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/40594/gfaviz-flexible-and-interactive-visualization-of-gfa-sequence-graphs Thu, 23 Jan 2020 07:33:46 -0600 https://bioinformaticsonline.com/bookmarks/view/40594/gfaviz-flexible-and-interactive-visualization-of-gfa-sequence-graphs <![CDATA[GfaViz: flexible and interactive visualization of GFA sequence graphs]]> GFA (Graphical Fragment Assembly) is an emerging standard format for representing sequence graphs. Although it was originally conceived as a format for sequence assembly (hence the name), and this remains its core application, it is more general, and able to represent many different types of sequence graphs, including scaffolding graphs, alignment graphs, variant graphs and splicing graphs.

Address of the bookmark: https://github.com/ggonnella/gfaviz

]]> Jit https://bioinformaticsonline.com/news/view/41562/submit-your-sars-cov-2-sequence-data-to-genbank Thu, 09 Apr 2020 18:28:25 -0500 https://bioinformaticsonline.com/news/view/41562/submit-your-sars-cov-2-sequence-data-to-genbank <![CDATA[Submit your SARS-CoV-2 sequence data to GenBank]]> Submit your SARS-CoV-2 sequence data to GenBank and SRA with our new submission landing page. Submission is simple and streamlined *and* there’s a rapid turnaround. https://submit.ncbi.nlm.nih.gov/sarscov2/
 
Quickly and easily add your SARS-CoV-2 sequence data to the growing public archive with new, special features and support from NCBI. new SARS-CoV-2 sequence submission landing page will help you get started. GenBank submissions are accessioned and released in approximately 1-2 working days, and Sequence Read Archive (SRA) submissions typically processed and released within hours. Submission is simple!
]]> Neel https://bioinformaticsonline.com/opportunity/view/24178/essentials-of-statistics-and-data-analysis-using-r Mon, 31 Aug 2015 01:32:12 -0500 <![CDATA[Essentials of Statistics and Data Analysis using R]]> Clinical Development Services Agency (CDSA) is an extramural unit of Translational Health Science and Technology Institute (THSTI), Department of Biotechnology, Ministry of Science & Technology, Government of India. CDSA has a national mandate of strengthening capacity and capability building in the area of Clinical development and Translational Research.

CDSA is pleased to announce a 4 days hands-on training program on “Essentials of Statistics and Data Analysis using R” at ICGEB, Aruna Asaf Ali Road, New Delhi on December 1 – 4, 2015. This will involve developing and enhancing skills to understand basic principles of statistics for summarizing data and use of appropriate statistical tests as well as providing an understanding of data analysis using R. Didactic lectures with practical sessions will be delivered by experienced faculties from AIIMS and Novartis. Live classroom with power point presentations, case studies, mock exercise, practical sessions on R, group work with time for discussion and Q&A sessions are added advantages of this workshop.

Please contact gayatrivishwakarma.cdsa@thsti.res.in or vineetabaloni.cdsa@thsti.res.in for program and registration details.

Please nominate personage or register yourself on or before November 6, 2015 along with the electronic transfer of registration fee.

]]> https://bioinformaticsonline.com/bookmarks/view/42645/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite Mon, 18 Jan 2021 10:47:56 -0600 https://bioinformaticsonline.com/bookmarks/view/42645/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

]]> Manisha Mishra https://bioinformaticsonline.com/opportunity/view/24298/staff-scientists-at-national-institute-of-plant-genome-research-new-delhi Fri, 04 Sep 2015 22:06:59 -0500 <![CDATA[Staff Scientists at National Institute of Plant Genome Research, New Delhi]]> National Institute of Plant Genome Research, New Delhi is an Autonomous Research Institution funded by Department of Biotechnology, Ministry of Science & Technology, Govt. of India, to pursue research on various aspects of plant genomics. The Institute is also in the process of establishing a NIPGR Translational Centre at Biotech Science Cluster, NCR, Faridabad. NIPGR invites applications from Indian Citizens for filling up the vacant posts on Direct Recruitment basis, as detailed below. The posts are temporary but likely to continue.

Staff Scientists

Specialization: Applicant should have a Ph.D. with excellent academic credentials along with the track record of scientific productivity evidenced by publications/patents/products in the frontier areas of Plant Biology such as, Computational Biology, Genome Analysis and Molecular Mapping, Molecular Mechanism of Abiotic Stress Responses, Nutritional Genomics, Plant Development and Architecture, Plant Immunity, Molecular Breeding, Transgenics for crop improvement and other emerging areas based on plant genomics.

Remuneration: The length of experience and scientific accomplishments/quality of scientific productivity record will be major factors in deciding the level of appointment as Staff Scientist as well as starting salary in the Pay Bands of Rs 15,600-39,100 (with grade pay of 5400), and Rs 37,400-67,000 (with grade pay of 8,700 and 8,900) plus usual allowances admissible to the Central Government employees. However, NIPGR reserves the right to select candidates in the lower grade against the foregoing posts depending upon the qualifications and experience of the candidate. Reservation of posts shall be as per Govt. of India norms. Five posts (SC-2, ST-1, OBC-2) in the Pay Band of Rs 15,600-39,100 with Grade Pay of Rs 5400, are reserved.

More at http://www.nipgr.res.in/careers/vacancies_latest.php#

Apply online at http://www.nipgr.res.in/nipgr_recu/nipgr_recu.php

Form http://www.nipgr.res.in/files/careers/Application_Performa_2015.doc

]]> https://bioinformaticsonline.com/bookmarks/view/44529/contigextender-a-new-approach-to-improving-de-novo-sequence-assembly-for-viral-metagenomics-data Wed, 08 May 2024 07:32:45 -0500 https://bioinformaticsonline.com/bookmarks/view/44529/contigextender-a-new-approach-to-improving-de-novo-sequence-assembly-for-viral-metagenomics-data <![CDATA[ContigExtender: a new approach to improving de novo sequence assembly for viral metagenomics data]]> ContigExtender, was developed to extend contigs, complementing de novo assembly. ContigExtender employs a novel recursive Overlap Layout Candidates (r-OLC) strategy that explores multiple extending paths to achieve longer and highly accurate contigs. ContigExtender is effective for extending contigs significantly in in silico synthesized and real metagenomics datasets.

More at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953547/

extension process

Address of the bookmark: https://github.com/dengzac/contig-extender

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs Fri, 03 Mar 2017 05:15:07 -0600 https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs <![CDATA[MetaPred2CS]]> MetaPred2CS Web server is a meta-predictor based on Support Vector Machine (SVM) that combines 6 individual sequence based protein-protein interaction prediction methods to predict prokaryotic two-component system protein-protein interactions (PPIs). The methods implemented in MetaPred2CS are 2 co-evolutionary methods: in-silico two hybrid (i2h) and mirror tree (MT) methods and 4 genomics context based methods: phylogenetic profiling (PP), gene fusion (GF), gene neighbourhood (GN) and and gene operon methods (GO).

 http://metapred2cs.ibers.aber.ac.uk/

Address of the bookmark: https://github.com/martinjvickers/MetaPred2CS

]]> Manisha Mishra https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite Wed, 03 Jan 2018 05:05:46 -0600 https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite <![CDATA[Seal: SEquence ALignment evaluation suite]]> Seal is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:

  • Reading a pre-existing reference genome from one or more FASTA files.
  • Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).
  • Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.
  • Applying alignment tools to the genome and the reads through a standardized interface.
  • Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.
  • Computing run times and measures of accuracy.

Seal has interfaces to evaluate the following software packages:

  • Bowtie
  • BWA
  • MAQ
  • mrFAST
  • mrsFAST
  • Novoalign
  • SHRiMP
  • SOAPv2

Address of the bookmark: http://compbio.case.edu/seal/

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