Interested applicants must submit CV and addresses of three references to ataulfo@unam.mx.

Tenure Track position in Genomic Sciences

Laboratorio Internacional de Investigación sobre el Genoma Humano, UNAM Juriquilla, Querétaro, México

The International Laboratory for Human Genome Research, LIIGH-UNAM (www.liigh.unam.mx) offers a tenure-track position at the level of Assistant Professor (Investigador Asociado C) to perform research, teaching and formation of human resources in the area of: “Genomics of Mendelian Diseases”

Applicants are expected to have a doctorate degree, postdoctoral experience related to the above mentioned area and a strong track record of peer-reviewed publications. Interested applicants must submit CV, email addresses of three references, and a three-page project to Dr. Rafael Palacios, Coordinator of LIIGH-UNAM (palacios@liigh.unam.mx) before June 21, 2019 ………………………………………………………………

Tenure Track position in Genomic Sciences

Laboratorio Internacional de Investigación sobre el Genoma Humano, UNAM Juriquilla, Querétaro, México

The International Laboratory for Human Genome Research, LIIGH-UNAM (www.liigh.unam.mx) offers a tenure-track position at the level of Assistant Professor (Investigador Asociado C) to perform research, teaching and formation of human resources in the area of: “Statistic Population Genomics and its Impact in Complex Diseases”

Applicants are expected to have a doctorate degree, postdoctoral experience related to the above mentioned area and a strong track record of peer-reviewed publications. Interested applicants must submit CV, email addresses of three references, and a three-page statement of research interests to Dr. Rafael Palacios, Coordinator of LIIGH-UNAM (palacios@liigh.unam.mx) before June 21, 2019

Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

#rasalsi #rasa In our Industrial program you will get Knowledge on RNA Seq, CHIP Seq,

Batch Starts From 18^th November 2019

#hurryup #registernow #enquirenow The primary goal of the industrial training program is to provide students with necessary skills making with employable. RASA LSI trains students with the enhanced skills required for them to excel in jobs in biotechnology, pharmaceuticals, BioIT and related industry sectors. At Rasa you will  learn from industry leaders how to apply these skills in life science & have a command over software developing process  by using various methodologies. For Registration visit us on: https://www.rasalsi.com/index.php/front-page/industrial-training/

https://github.com/zhangzhwlab/delta

Address of the bookmark: https://github.com/zhangzhwlab/delta

Requirements
Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus
Ability to numerically process complex and large data sets
Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)
Experience in analyzing next-generation sequencing data sets using network biology
Scientific publication record in applied bioinformatics
Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential

1. Supply your genes/genomic segments/phylogenetic tree of interest in the input-box by
   • selecting the type of identifier and pasting identifiers (one per line)
   • or by using the gene ID search tool
   • or with the BLAST search tool
2. Click "Visualize context".

Consult the documentation to learn more about MGcV.

Address of the bookmark: http://mgcv.cmbi.ru.nl/

An Institution of National Importance established by Dept. of
Biotechnology, Govt. of India Under the auspices of UNESCO

Advertisement No. RCB/IBDC/01/2020/Recruitment/HR

Recruitment For Contractual Positions Under The Project
Indian Biological Data Centre (IBDC) Phase-1

Regional Centre for Biotechnology (RCB) invites online applications from suitably qualified, dynamic, result-oriented and dedicated candidates for the following positions under the project Indian Biological Data Centre (BIDC) Phase-1 on contract basis:

Industry: Biotechnology

Location: Faridabad (Haryana, India)

Project Head - 1

Senior Scientist (Bioinformatics/ IT) - 1

For other details, visit: www.rcb.res.in. Last date for receipt of online application is 18th July 2020.

Registrar

Regional Centre for Biotechnology
Faridabad-Gurgaon Expressway,
Faridabad - 121001

• hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
• gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
• comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
• sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
• blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
• filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
  • kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
  • seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
• plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
  • density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
  • profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
  • spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
  • spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
  • spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339 

Address of the bookmark: https://github.com/TGAC/KAT

Pay Matrix Level-12
No. of Post-01
(UR)
Post – Sr. Scientist
Area Bioinformatics
Age limit : 37 years
PhD in Computational Biology/Bioinformatics with 2 years experience in desired area
Or
ME/M.Tech in Bioinformatics or Genome Informatics or Genetic Engineering with 3 years experience in desired area
Experience of understanding fundamental science behind Artificial Intelligence, machine learning, novel Artificial Intelligence algorithms and architectures, software engineering principles for Artificial Intelligence, natural language processing with proficiency in programming as evident by publications in SCI journals with high impact factor. To be part a group of scientists working in the area of genomics, running the central
bioinformatics facility, developing independent projects and providing bioinformatics support to the user scientists of the Institute.

More at

http://14.139.62.50/CSIR-IITR%20Scientist%20Recruitment%20Adv%202020.pdf

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant