<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/24264?offset=990 https://bioinformaticsonline.com/bookmarks/view/43685/chipbase-open-database-for-studying-the-transcription-factor-binding-sites-and-motifs Wed, 29 Dec 2021 05:36:03 -0600 https://bioinformaticsonline.com/bookmarks/view/43685/chipbase-open-database-for-studying-the-transcription-factor-binding-sites-and-motifs <![CDATA[ChIPBase: open database for studying the transcription factor binding sites and motifs]]> ChIPBase v2.0 is an open database for studying the transcription factor binding sites and motifs, and decoding the transcriptional regulatory networks of lncRNAs, miRNAs, other ncRNAs and protein-coding genes from ChIP-seq data. Our database currently contains ~10,200 curated peak datasets derived from ChIP-seq methods in 10 species.

Address of the bookmark: https://rna.sysu.edu.cn/chipbase/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/3868/next-generation-sequencing-ngs-tutorials Sat, 24 Aug 2013 06:01:37 -0500 https://bioinformaticsonline.com/bookmarks/view/3868/next-generation-sequencing-ngs-tutorials <![CDATA[Next Generation Sequencing (NGS) Tutorials]]> Institute of computational biomedicine, Cornell University provide an NGS workshop tutorial at http://chagall.med.cornell.edu/NGScourse/ 

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit. 

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

==============================================

NGS data analysis pipelines

  • Detecting and annotating genetic variations using the HugeSeq pipeline  DOI: 10.1038/nbt.2134
  • NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
  • RseqFlow: Workflows for RNA-Seq data analysis  DOI: 10.1093/bioinformatics/btr441
  • ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence  10.1186/1471-2164-12-285
  • A framework for variation discovery and genotyping using next-generation DNA sequencing data  PubMed: 21478889
  • SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects  DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
  • WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
  • DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
  • GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
  • Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
  • RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
  • Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
  • Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
  • Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
  • Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
  • RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ". 

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

]]> Jitendra Narayan https://bioinformaticsonline.com/file/view/4219/brief-biodata-of-dr-pranjal-chandra Wed, 04 Sep 2013 11:14:03 -0500 https://bioinformaticsonline.com/file/view/4219/brief-biodata-of-dr-pranjal-chandra <![CDATA[Brief Biodata Of Dr. Pranjal Chandra]]> Pranjal Chandra PhD https://bioinformaticsonline.com/pages/view/18749/list-of-biotechnology-research-laboratory-university-and-centre-in-india Thu, 30 Oct 2014 12:34:34 -0500 https://bioinformaticsonline.com/pages/view/18749/list-of-biotechnology-research-laboratory-university-and-centre-in-india <![CDATA[List of Biotechnology Research Laboratory, University, and Centre in India]]> DEPARTMENT OF BIOTECHNOLOGY

INDIAN COUNCIL OF MEDICAL RESEARCH (ICMR)

COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH (CSIR)

 

CITYWISE

City Wise Llist of Govt. and Non Govt. Research institutions in India -

Ahmedabad

Allahabad

Bangalore

Barrackpore (W. B.)

· Central Inland Capture Fisheries Research Institute

Bhopal

Bhubaneswar

Calcutta

Chandigarh

Chennai (Madras)

Delhi

Dehradun

Dirang (Arunachal Pradesh)

Durgapur

Eluru (A. P.)

Gandhi Nagar

Goa

Hyderabad

Indore

Jammu

Jhansi (U. P)

Jodhpur (Rajasthan)

Kanpur

Kasaragod

Kharagpur

Kochi

Lucknow

Mathura

Mumbai (Bombay)

Palakkad

Palampur (H.P.)

Peechi

Pilani

Puttur (Karnataka)

Pune

Roorkie (U. P.)

Trivandrum (Thiruvananthapuram)

Varanasi (U. P.)

· Indian Institute of Vegetable Research

 

UNIVERSITIES/COLLEGES UNDER UGC

DST

•  Agharkar Research Institute, Pune

•  Birbal Sahni Institute of Palaeobotany, Lucknow

•  Bose Institute, Kolkata

•  Indian Association for the Cultivation of Science, Kolkata

•  International Advanced Research Centre for Powder Metallurgy and New Materials,
...esr ...Hyderabad

•  Indian Institute of Astrophysics, Bangalore

•  Indian Institute of Geomagnetism, Mumbai

•  Indian Institute of Tropical Meteorology, Pune

•  Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore

•  National Accreditation Board for Testing & Calibration Laboratories, New Delhi

•  Raman Research Institute, Bangalore

•  S.N. Bose National Centre for Basic Sciences, Kolkata

•  Sreechitra Tirunal Institute for Medical Sciences & Technology, Thiruvananthapuram

•  Technology Information, Forecasting & Assessment Council (TIFAC), New Delhi

•  Vigyan Prasar, New Delhi

 

Indian Council of Agricultural Research

  • National Bureaue of Soil Survey & Land Use Planning (NBSSLUP)
  • Central Research Institute for Jute & Allied Fibres (CRIJAF)
  • National Bureaue of Agriculturally Important Micro-Organisms (NBAIMO
  • Central Research Institute for Dryland Agriculture
]]> Jit https://bioinformaticsonline.com/opportunity/view/42418/scientist-b-bioinformatics-at-aiims-delhi Sun, 20 Dec 2020 04:34:55 -0600 <![CDATA[Scientist-B (Bioinformatics) at AIIMS, Delhi]]> Name of the Project: “Artificial intelligence in Oncology, Harnessing big data and advanced computing to provide personalized diagnosis and treatment for Cancer patients”

Age Limit: 35

How to Apply for the AIIMS Life Science Job:

Interested applicants are asked to send out a detailed CV to Dr Ashok Sharma (aioncoaiims@gmail.com). Laboratory of Chromatin and also Cancer Epigenetics, Department of Biochemistry with the subject line “Application for Scientist-B position for MeitY project” latest by January 01st, 2021.
Complete Information of the year of passing, experience, marks, etc. ought to be mentioned in the CV Incomplete. applications will certainly be rejected Just shortlisted applicants will be called for interview. Chosen candidates will certainly be intimated by email/phone.
No TA/DA will certainly be paid for appearing in the interview.
Note, The institute reserved the right to fill up or not to fill up the post advertised.

Emoluments: Rs. 56,000/- plus 24 percent HRA

Eligibility:
2nd class Master’s Degree with a PhD in a pertinent subject (Bioinformatics) from.a recognized University
1st class Master’s degree in Life Sciences (Bioinformatics) from a recognized university OR.
Bachelor’s Degree in Engineering or-Technology with minimal 60% marks from a recognized University or equivalent.

Desirable Qualifications:
Experience in Bioinformatics/NGS data. Analysis/System Biology/Computer Science/ statistics with experience in Machine learning/Al project.
Experience of Deep learning applications in biological data ( image/text).
Proficient in Rf Python machine learning libraries.
Prior experience in the cancer-related project (ML-based) will be advantageous.
Experience with PyTorch/TensorFlow will certainly be very desirable.
Applicant should have strong scientific writing as well as. verbal abilities.
Papers in sci-indexed journals demonstrating ML skill sets.
Database handling will certainly be plus yet not required.

More detail at https://www.aiims.edu/images/pdf/recruitment/advertisement/biochem-16-12-20.pdf

]]> https://bioinformaticsonline.com/bookmarks/view/38804/grabb-selective-assembly-of-genomic-regions-a-new-niche-for-genomic-research Sat, 26 Jan 2019 18:58:16 -0600 https://bioinformaticsonline.com/bookmarks/view/38804/grabb-selective-assembly-of-genomic-regions-a-new-niche-for-genomic-research <![CDATA[GRAbB: Selective Assembly of Genomic Regions, a New Niche for Genomic Research]]> GRAbB is shown to be more efficient than MITObim in terms of speed, memory and disk usage. The other functionalities (handling multiple targets simultaneously and extracting homologous regions) of the new program are not matched by other programs. The program is available with explanatory documentation at https://github.com/b-brankovics/grabb. GRAbB has been tested on Ubuntu (12.04 and 14.04), Fedora (23), CentOS (7.1.1503) and Mac OS X (10.7). Furthermore, GRAbB is available as a docker repository: brankovics/grabb (https://hub.docker.com/r/brankovics/grabb/).

Address of the bookmark: https://github.com/b-brankovics/grabb

]]> Rahul Nayak https://bioinformaticsonline.com/researchlabs/view/42903/katherine-belov-lab Sun, 21 Feb 2021 22:59:35 -0600 <![CDATA[Katherine Belov Lab]]> Evolution of the adaptive immune system Marsupial and monotreme immune genes MHC Diversity and Conservation Marsupial and monotreme genomics Comparative Genomics Genetics of Tasmanian Devil facial tumour disease

More at https://www.sydney.edu.au/science/about/our-people/academic-staff/kathy-belov.html

]]> https://bioinformaticsonline.com/news/view/45351/ai-uncovers-hidden-secrets-in-bacterial-dna-opening-new-frontiers-in-genomic-research Fri, 25 Sep 2026 22:38:42 -0500 https://bioinformaticsonline.com/news/view/45351/ai-uncovers-hidden-secrets-in-bacterial-dna-opening-new-frontiers-in-genomic-research <![CDATA[AI Uncovers Hidden Secrets in Bacterial DNA, Opening New Frontiers in Genomic Research]]> Scientists are now using artificial intelligence in order to examine sections of bacterial DNA that have not been looked at before. This method is showing potential RNA interactions and previously unknown genetic systems which could alter our understanding of microbes. A new study presents Minerva, a genome language model, demonstrating that AI can assist researchers in identifying biological patterns that traditional techniques might fail to detect.
The study, which was made available as a preprint on bioRxiv on 23 September 2026, focuses on the non-coding sections of DNA that are still largely unknown. Although these areas do not produce proteins, they can contain important signals and instructions which have an effect on cell function. The research presents a novel approach to investigating how bacteria handle and control their genetic information.
AI maps previously unexplored genomic regions
The team developed Minerva in order to identify possible interactions between different regions in microbial genomes; rather than depending on similarities with known sequences, Minerva predicts these relationships directly from the DNA by using patterns learned by a genome language model.
On 150 bacterial genomes, Minerva identified a large number of interactions that were not included in the existing annotations. The researchers stated that 84.3 per cent of the predicted intergenic base-pairing interactions were not present in the current annotations, which demonstrates that AI can be of help in generating new ideas in biology.
Unusual RNA structures and viral genetic systems identified
The study also examined a bacterial RNA family known as TwoAYGGAY in Pseudomonas; the model anticipated longer RNA structures and identified associations with repeated DNA sequences, thus providing new insights into how these non-coding elements are organised and how they have evolved.
In a separate section of the study, the researchers examined reverse transcriptase systems associated with bacteriophages, which are viruses that infect bacteria. They identified RNA arrays that maintain their structure but have different sequences and were linked to Unknown Group 27 reverse transcriptases. The findings indicate that these RNAs could function as templates for the production of complementary DNA that is capable of forming hairpin shapes.
The researchers also observed that Minerva was able to detect patterns associated with protein-coding areas, even though it had not been trained to do so. This indicates that genome language models may pick up on biological signals that go beyond what they were intended to identify.
Implications for future genomic research
The fact that artificial intelligence is becoming increasingly important in the field of microbial genomics is shown by the fact that models such as Minerva are able to predict interactions and identify patterns in areas which have not been extensively studied, thus helping researchers to decide what to study next and enabling them to gain a better understanding of biological systems that are still not well understood.
Yet the predictions do not reveal the exact function of each element identified. In order to verify which of the predicted interactions actually take place in living cells and the way in which they affect the microbes, experiments will be necessary. Although the study has undergone peer review, it does nonetheless offer a promising illustration of how machine learning can complement traditional genomics and assist scientists in moving from the identification of known genes to the exploration of the complex relationships that shape microbial life.
More at https://www.biorxiv.org/content/10.64898/2026.09.22.753630v2.full.pdf
 
]]> Jitendra Narayan https://bioinformaticsonline.com/file/view/43092/where-to-aproach-for-rd-funds-in-india Thu, 24 Jun 2021 01:04:30 -0500 https://bioinformaticsonline.com/file/view/43092/where-to-aproach-for-rd-funds-in-india <![CDATA[Where to Aproach for R&D Funds in India ?]]> Companies and governments do research and development (R&D/ R'n'D/ R+D) to promote innovation in order to produce new goods or services and/or enhance existing product lines. R&D covers all actions inside an organization aimed at boosting innovation, such as creating incubators, assisting innovators in scaling up their ideas, and cultivating an innovation culture.

Here are the list of all the research and development funding agencies in India.

]]> Surabhi Chaudhary 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

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