Address of the bookmark: https://github.com/lh3/ropebwt2

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

Applications are invited for the posts of one Junior Research Fellow in the DBT funded project entitled “Plant parasitic nematode genome informatics - insilico resource development”. The project is for a period of three years.

Essential qualifications for JRF: First class M. Sc. / M. Tech in Bioinformatics. Knowledge of programming language, pearl, Statistics and database – HTML, CSS, Java script.

Desirable qualifications: Experience in handling next generation sequencing data

Age limit: 35 years maximum (5 year relaxation for SC/ST and women candidates) Emoluments: 16,000 + 30% HRA.

The post is purely temporary in nature and is co-terminus with the project. The appointment would be initially for one year and may be extended further upon satisfactory performance.

Those who are interested in pursuing Ph.D with strong research aptitude are preferred.

Interested candidates may attend the Walk in interview on 25th March 2014 along with the duly filled application forms (format in the following page) with all the relevant documents.

Venue: Division of Nematology, Indian Agricultural Research Institute, New Delhi 110012 (Room No. 306, 3rd floor, LBS building)

Reporting Time: Interested candidates should report strictly between 10.00 to 10.30 AM.

Interview time: 10.30 AM

Short-listed candidates will be called for interview at New Delhi. However, no TA and DA will be paid for attending the interview.

Advertisement:

https://www.iari.res.in/files/JRF_Nema-07032014-20140307-170017.pdf

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

is a state-funded virtual research center aimed at promoting imaging and informatics research infrastructure in South Dakota. BioSNTR research foci include analysis of large-scale genomics and imaging data, application of novel microscopy technologies to study signaling pathways, and identification of new compounds to manipulate signaling pathways.
Responsibilities: This person will be part of Plant Science team with research focus in bioinformatic and molecular network analyses of high throughput data (transcriptomic, proteomic, metabolomics, miRNA). The individual will be integrated into functional genomic projects encompassing grapevine dormancy and freezing tolerance (Fennell) and regulation of soybean nodulation (Subramanian). The successful candidate will perform computational analysis of high throughput, next-generation sequence data and possess the ability to use bioinformatics analytical tools on HPC clusters.

Required Qualifications:
• Ph.D. in plant computational biology or bioinformatics.
• Experience in a high performance computing environment.
• Perl, Python and Java programming experience
• Data management and database development experience

Desired Qualifications:
• Parallel computing experience
• Experience working in a multidisciplinary environment

Contact Information
South Dakota State University
Plant Science
Anne Fennell
anne.fennell@sdstate.edu
Tel. Number: 605-688-6373
http://www.biosntr.org

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

In our day to day research activity, we need to securely copy our data from several to local computer and visa-versa. I am jotting down some of the commonly used SCP command for your future help. Hope you all will like it

What is Secure Copy?

scp allows files to be copied to, from, or between different hosts. It uses ssh for data transfer and provides the same authentication and same level of security as ssh.

Examples

Copy the file "gene.txt" from a remote host to the local host

    $ scp your_username@remotehost.edu:gene.txt /some/local/directory

Copy the file "foobar.txt" from the local host to a remote host

    $ scp gene.txt your_username@remotehost.edu:/some/remote/directory

Copy the directory "chromosome" from the local host to a remote host's directory "bar"

    $ scp -r chromosome your_username@remotehost.edu:/some/remote/directory/bar

Copy the file "gene.txt" from remote host "rh1.edu" to remote host "rh2.edu"

    $ scp your_username@rh1.edu:/some/remote/directory/gene.txt \
    your_username@rh2.edu:/some/remote/directory/

Copying the files "gene.txt" and "cancer.txt" from the local host to your home directory on the remote host

    $ scp gene.txt cancer.txt your_username@remotehost.edu:~

Copy the file "gene.txt" from the local host to a remote host using port 2264

    $ scp -P 2264 gene.txt your_username@remotehost.edu:/some/remote/directory

Copy multiple files from the remote host to your current directory on the local host

    $ scp your_username@remotehost.edu:/some/remote/directory/\{a,b,c\} .

    $ scp your_username@remotehost.edu:~/\{gene.txt,cancer.txt\} .

scp Performance

By default scp uses the Triple-DES cipher to encrypt the data being sent. Using the Blowfish cipher has been shown to increase speed. This can be done by using option -c blowfish in the command line.

    $ scp -c blowfish some_file your_username@remotehost.edu:~

It is often suggested that the -C option for compression should also be used to increase speed. The effect of compression, however, will only significantly increase speed if your connection is very slow. Otherwise it may just be adding extra burden to the CPU. An example of using blowfish and compression:

    $ scp -c blowfish -C local_file your_username@remotehost.edu:~

There are Neanderthal bones. Denisovan remains. Fossils from ancient relatives of our species. Each specimen gives us another piece of the story of how Homo sapiens came to be.

But now imagine that one of the most important characters in that story has left behind no fossil, no skull, no bone, and no genome.

Nothing that a paleontologist could put behind glass.

And yet, somehow, that ancient population is still speaking.

It is speaking through us.

A fascinating new study published in Science (https://www.science.org/doi/10.1126/science.aef8874?) introduces a computational method called TRACE, which allows scientists to search modern human genomes for genetic traces of ancient populations that have never been directly identified.

For years, scientists have known that our ancestors interacted with Neanderthals and Denisovans. Their DNA still survives in modern human populations. But these may not have been the only ancient humans who crossed paths with our ancestors.

Using TRACE, researchers found evidence of an unknown archaic population that contributed DNA to the ancestors of modern humans before humans spread beyond Africa.

Even more surprisingly, they found evidence of a much older lineage—possibly around 1.8 million years old—whose genetic legacy may have reached some modern humans indirectly through Denisovans.

The extraordinary part is that researchers did not need DNA from these mysterious populations to detect them. Instead, they studied the ancestry of modern genomes and looked for genetic patterns that revealed unusually ancient branches of our family history.

It is almost like finding the footprints of someone who disappeared thousands of years ago—without ever finding the person themselves.

These discoveries suggest that human evolution was far more complicated than a simple family tree. Ancient human populations repeatedly separated, migrated, met and exchanged genes. Some eventually disappeared, but pieces of their genetic legacy survived.

And that means our DNA is more than a biological instruction manual.

It is also an archive of human history.

Some of the people recorded in that archive may never have a name or a fossil. But thanks to methods like TRACE, their genetic echoes are finally becoming visible.

The next big discovery about our ancient past may not come from a fossil in the ground.

It may already be inside us.

Reference: Zhang et al., “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” Science (2026), DOI: 10.1126/science.aef8874. Detail paper @ https://www.science.org/doi/10.1126/science.aef8874?

The TRACE repository (https://github.com/YulinZhang9806/trace) contains the Python package and command-line tools used to infer archaic admixture tracts from ancestral recombination graphs.

We are looking for a motivated candidate for handling proteomic and/or transcriptomic and other data with a strong background in bioinformatics tools and database development. The project will include identification of novel peptides from mass spectrometry-based proteomic data.

Familiarity with statistical tools or wet lab experience will be an added advantage. The position is open for immediate appointment and available for two years. The applicant will be appointed as Research Associate or Senior Research Fellow based on qualifications as detailed below:

Research Associate: Ph.D. in Biological Science or Bioinformatics with relevant publications in peer reviewed journals. Familiarity with bioinformatics tools, database development, programming skills and proteomic and/or other omics data analysis. Salary will be as per ICMR rules and guidelines.

Senior Research Fellow: M.Sc./B.Tech. in any branch of biology/ biotechnology/bioinformatics, with minimum 2 years of research experience (essential). Familiarity with bioinformatics tools, database development, programming skills and proteomic data analysis. Salary will be as per ICMR rules and guidelines.

Application will be shortlisted based on CV, reference letters from mentors and telephonic interview. Candidates will be called for a personal interview at Bangalore before appointment. No travel expense will be provided for attending interview at Bangalore.

Interested candidates may send a Letter of Interest and CV by email to: ravi@ibioinformatics.org on or before May 15th, 2014.

Contact:
Dr. Ravi Sirdeshmukh
Distinguished Scientist & Associate Director, IOB,
Principal Advisor MSMC/MSCTR

Advertisement: www.ibioinformatics.org/careers.php

Awards of $5 million to four grantees have been made in fiscal year 2013 under the Genomic Sequencing and Newborn Screening Disorders research program. The program will be funded at $25 million over five years, as funds are made available.

"Hundreds of US babies will be pioneers in genomic medicine through a US$25-million programme to sequence their genomes soon after they are born."

Source:

http://blogs.nature.com/news/2013/09/scientists-to-sequence-hundreds-of-newborns-genomes.html

http://www.genome.gov/27554919