<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/23680?offset=1170 https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data Tue, 27 Nov 2018 04:43:57 -0600 https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data <![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]> lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.

lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

 

Address of the bookmark: https://github.com/vpc-ccg/lordfast

]]> BioJoker https://bioinformaticsonline.com/bookmarks/view/40613/genome-in-a-bottle-giab-consortium Sat, 25 Jan 2020 13:50:52 -0600 https://bioinformaticsonline.com/bookmarks/view/40613/genome-in-a-bottle-giab-consortium <![CDATA[Genome in a Bottle (GIAB) Consortium]]> The Genome in a Bottle (GIAB) Consortium is a public-private-academic consortium hosted by NIST to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice.

https://www.nist.gov/news-events/news/2016/09/nist-releases-new-family-standardized-genomes

Address of the bookmark: https://jimb.stanford.edu/giab/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/42581/autogluon-automl-for-text-image-and-tabular-data Thu, 07 Jan 2021 05:33:17 -0600 https://bioinformaticsonline.com/bookmarks/view/42581/autogluon-automl-for-text-image-and-tabular-data <![CDATA[AutoGluon: AutoML for Text, Image, and Tabular Data]]> AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on text, image, and tabular data.

Address of the bookmark: https://github.com/awslabs/autogluon

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44742/nasa-open-science-data-repository Wed, 18 Dec 2024 11:54:47 -0600 https://bioinformaticsonline.com/bookmarks/view/44742/nasa-open-science-data-repository <![CDATA[NASA Open Science Data Repository]]> The NASA Open Science Data Repository (OSDR) enables access to space-related data from experiments and missions that investigate biological and health responses of terrestrial life to spaceflight. The goal of OSDR is to enable multi-modal and multi-hierarchical fundamental space life science data be reused toward basic science, applied science, and operational outcomes for space exploration and knowledge discovery. These data include ‘omics, phenotypic, physiological, behavioral, hardware, environmental telemetry; raw, processed; tabular, text, code, bioimaging, and video.

https://www.nasa.gov/reference/osdr-data-processing/

Address of the bookmark: https://www.nasa.gov/osdr/

]]> Abhi https://bioinformaticsonline.com/opportunity/view/42294/the-hiller-lab-at-the-loewe-tbg-in-frankfurt-is-looking-for-an-ambitious-postdoc%E2%80%93-comparative-genomics Sat, 31 Oct 2020 23:01:41 -0500 <![CDATA[The Hiller Lab at the LOEWE-TBG in Frankfurt is looking for an ambitious PostDoc– Comparative Genomics]]> The mission of our group is to discover genomic determinants of phenotypic differences between species, which is important to understand how nature's fascinating phenotypic diversity evolved and how it is encoded in the genome. Work in the lab ranges from genome assembly and alignment to annotating genes, developing and applying comparative genomic methods to discover key differences in genes (such as loss, gain, selection) and regulatory elements, and using statistical approaches to associate genomic to phenotypic differences [1-8].

The postdoc will join our efforts to extend our methods repertoire to accurately detect additional types of genomic changes, to adopt them to other taxonomic groups, and to apply them on a large-scale to existing and numerous newly-sequenced genomes generated by us and our TBG collaborators.

Candidate requirements
· PhD degree in bioinformatics / computational biology, genomics or a related area

· a strong publication record

· excellent programming skills in a Linux environment as well as experience with shell scripting and Unix tools

· previous experience in large-scale comparative genomic data analysis is an advantage.

More at https://www.lifescience.net/jobs/71529/postdoc-mfd-comparative-genomics/

]]> https://bioinformaticsonline.com/opportunity/view/17504/postdoc-scientist-bioinformatics-at-ccmb Fri, 26 Sep 2014 19:58:41 -0500 <![CDATA[PostDoc Scientist Bioinformatics at CCMB]]> 1. Project Assistant/Junior Research Fellow/ Project Fellow [PA_JRF_PF]

a) M.Sc/or equivalent in biological sciences/related areas [Position Code: PA_JRF_PF_a]
b) B.E/B.Tech/ M.Sc in biotechnology/bioinformatics/computer science/Chemistry/Physics or MCA [Position Code: PA_JRF_PF_b]
c) M.Sc/or equivalent in wildlife sciences/ecology/environmental sciences or MBBS/BVSc/MVSc. [Position Code: PA_JRF_PF_c]

(Candidates with result awaited are NOT eligible to apply)

Upper Age limit 28years

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

2. Post Doctoral Fellow/Research Associate in multiple research areas [PDF_RA]

Ph.D. (submitted/awarded) in any branch of biological Sciences. Candidates with Ph.D. in other sciences are also encouraged to apply.

Experience in molecular biology, biochemistry, structural biology, cell biology, infectious disease, conservation genetics, veterinary science, reproductive biology, and molecular diagnostics is desired but not mandatory.

[Position Code: PDF_RA]

UpperAge limit 35years

Rs. 22000- 26000 (as sanctioned by the funding agency)

3. Post Doctoral Scientist Fellow [PDSF]

Ph.D in any of the following areas: bioinformatics, next generation sequencing, high throughput data analysis, proteomics, bio-statistics, computer science, information technology, computer hardware and networking/clustering, parallel processing.
[Position Code: PDSF]

Upper Age limit 40 years

Rs. 40000 consolidated (as sanctioned by the funding agency)

Download Application: Last date for apply online: 09th Oct 2014

Advertisement: www.ccmb.res.in//index.php?view=notifications&mid=0&id=71&nid=38

Apply online http://www.ccmb.res.in/positions/temp_notif/online_form.html

More at http://www.ccmb.res.in//index.php?view=notifications&mid=0&id=71&nid=38

]]> https://bioinformaticsonline.com/bookmarks/view/36827/sex-detector-a-probabilistic-approach-to-study-sex-chromosomes-in-non-model-organisms Wed, 30 May 2018 15:57:31 -0500 https://bioinformaticsonline.com/bookmarks/view/36827/sex-detector-a-probabilistic-approach-to-study-sex-chromosomes-in-non-model-organisms <![CDATA[SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms]]> SEX-DETector is a probabilistic method that relies on RNAseq data from a cross (parents and progeny of each sex) to infer autosomal and sex-linked genes (genes located on the non recombining part of sex chromosomes).

How does SEX-DETector work?

SEX-DETector does not require prior sequencing of a reference genome: the same sequencing data can be used for the assembly and for the mapping of the reads. A full documentation on the pipeline can be found here.

  • we recommend Trinity for the assembly.
  • Trinity components should be merged with cap3. Our code to perform the merging is available here.
  • We recommend BWA for mapping of the reads.
  • When the mapping has been perfomed, the individuals need to be genotyped; SEX-DETector takes files produced by Reads2snp (which is available for download on the PopPhyl website) as input.

Address of the bookmark: http://lbbe.univ-lyon1.fr/-SEX-DETector-.html?lang=eg

]]> Surabhi Chaudhary https://bioinformaticsonline.com/researchlabs/view/17652/arraygen-bioinformatics-genomics-group Sun, 28 Sep 2014 14:09:55 -0500 <![CDATA[ArrayGen Bioinformatics Genomics Group]]> ArrayGen is a global bioinformatics company which is a one stop solution for microarray designing and genomics data analysis. Our novel Array Design Approach Strategy (ADAS) aims to condense the time lag between demands of scientific community and manufacture industry, thereby expediting research processes.

ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.

More http://www.arraygen.com/

]]> https://bioinformaticsonline.com/bookmarks/view/37584/mulan-multiple-sequence-local-alignment-and-visualization-for-studying-function-and-evolution Fri, 24 Aug 2018 09:50:01 -0500 https://bioinformaticsonline.com/bookmarks/view/37584/mulan-multiple-sequence-local-alignment-and-visualization-for-studying-function-and-evolution <![CDATA[Mulan: Multiple-sequence local alignment and visualization for studying function and evolution]]> Mulan: Multiple-sequence local alignment and visualization for studying function and evolution

Mulan (http://mulan.dcode.org/), a novel method and a network server for comparing multiple draft and finished-quality sequences to identify functional elements conserved over evolutionary time. Mulan brings together several novel algorithms: the TBA multi-aligner program for rapid identification of local sequence conservation, and the multiTF program for detecting evolutionarily conserved transcription factor binding sites in multiple alignments. In addition, Mulan supports two-way communication with the GALA database; alignments of multiple species dynamically generated in GALA can be viewed in Mulan, and conserved transcription factor binding sites identified with Mulan/multiTF can be integrated and overlaid with extensive genome annotation data using GALA.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC540288/

]]> Jit https://bioinformaticsonline.com/opportunity/view/17873/postdoc-position-in-protein-annotation-and-machine-learning-paris-france Sat, 04 Oct 2014 08:10:45 -0500 <![CDATA[Postdoc position in protein annotation and machine learning - Paris, France]]> We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of Paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

The postdoc will be payed under a contract of Ingénieur de Recherche lasting 3.5 years and it is available from September 1st, 2014.

Group Web Page: http://www.lcqb.upmc.fr/AnalGenom/home.html

Ref. E-Mail: Alessandra Carbone alessandra.carbone@lip6.fr

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