BOL: Related items

Pacasus: Correction of palindromes in long reads from PacBio and Nanopore

BioStar — Mon, 12 Nov 2018 05:26:48 -0600

Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder: https://github.com/swarris/Pacasus/blob/master/vib2017.pdf.

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

SENIOR RESEARCH FELLOW at All India Institute of Medical Sciences (AIIMS Delhi) - Delhi, Delhi

Wed, 11 Mar 2015 03:06:10 -0500

Applications are invited from eligible candidates for the following temporary post in an ICMR funded Research Project entitle “An Investigation to find out reasons for Phenotypic Heterogeneity/Variability in 22q11.2 Microdeletion Syndrome” in Department of Reproductive Biology, AIIMS, New Delhi PI: Dr. Ashutosh Halder, Professor, Department of Reproductive Biology

Name of the post: Senior Research Fellow (SRF)
Duration: 2 year
Salary: Rs. 28000/- per month + 30% HRA
Eligibility: MSc (life sciences) with 2 years research experience, NET/GATE qualified
Desirable: Experience in the field of Genomics, Epigenomics & Bioinformatics
SELECTION PROCEDURE FOR ALL INDIA INSTITUTE OF MEDICAL SCIENCES (AIIMS DELHI) – SENIOR RESEARCH FELLOW POST:

Candidates can apply on or before 15/03/2015
No Detailed information about the selection process is mentioned in the recruitment notification
HOW TO APPLY FOR SENIOR RESEARCH FELLOW VACANCY IN ALL INDIA INSTITUTE OF MEDICAL SCIENCES (AIIMS DELHI):

Deadline: 15.03.15 Submit your C.V in Room No. 2099 (Molecular Cytogenetics Lab), 2nd floor, Reproductive Biology, All India Institute of Medical Sciences, New Delhi-110029 or Email CV to: ashutoshhalder@gmail.com Your CV should include the details of your work experience & degrees along with two references with e-mail and contact number Only 10 shortlisted (on merit) candidates will be invited for interview. No TA/DA will be applicable for the same

ngs-bits - Short-read sequencing tools

Neel — Thu, 16 Jan 2020 23:14:00 -0600

Binaries of ngs-bits are available via Bioconda. Alternatively, ngs-bits can be built from sources:

Binaries for Linux/macOS
From sources for Linux/macOS
From sources for Windows

Address of the bookmark: https://github.com/imgag/ngs-bits

Research Associate and JRF positions in the Structural and Computational Biology Group at ICGEB

Mon, 02 Feb 2015 23:00:37 -0600

Research Associate and JRF positions in the Structural and Computational Biology Group starting 1st March 2015. Collaborative projects include work on:

a) bioinformatics, systems and computational biology
b) malaria
c) drug discovery
d) genomics
e) microbiology
f) metabolic disorders
g) molecular medicine

Eligibility: Applicants must have one of the following :

1) INSPIRE award for undertakig either PhD or Postdoctoral research;
2) SPM award for PhD;
3) JRF for pursuing PhD from CSIR/DBT/ICMR

Interest and experience in Biochemistry/Bioinformatics/Biophysics/ Chemistry/Genomics/Molecular Biology/ is essential.

Submit curriculum vitae to sb.icgeb@gmail.com by 20 February 2015

Which are the best statistical programming languages to study for a bioinformatician?

Jitendra Narayan — Wed, 10 Jul 2013 14:35:34 -0500

In Bio-informatics based genome sequencing and predicting metabolic pathways research jobs I used Matlab, SAS, SPSS, R and several Bioconductor packages. Matlab had a lot of powerful tools and was easy to use, whereas SPSS is for non-programmers and R need programming skills. I am wondering what other people think is best? or there might not be one specific language but a few that lend themselves best to Bio-informatics work that is math heavy and deals with a large amount of data.

RA Bioinformatics at North Eastern Hill University

Sat, 07 Feb 2015 06:06:05 -0600

Bioinformatics Infrastructure Facility, Department of RDAP, NEHU vacancy of Research Associate

Name of the Post: Research Associate
No. of the Post: 01 One
Age Limit: Max. 35 years
Salary: Rs. 22000/- per month plus HRA

Required Job Profile:
Candidate must possess M.Sc. in bioinformatics or biotechnology from recognized university or institute.
Desired Job Profile;
Candidate having Ph.D. or pursuing Ph.D. in the related subject or equivalent published work in reputed peer reviewed journals or advance PG dipoma in bioinformatics course.

How to apply:
Eligible and interested candidates should need to send the bio-data and bring all related documents in original and set of attested copies of the same in the time of interview.

Last date: 16.02.2015
Refer to http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf

Summary
Employer Address: Dr.B.K. Mishra Coordinator BIF, RDAP Department, North Eastern Hill University, Tura Campus, Tura, Meghalaya
Email: drbkm1972@yahoo.co.in;birendramishra14@gmail.com
URL: http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf
Phone: 03651-223107
Required Skills: not mentioned / required for this post
Required Experience: not mentioned / required for this job post
Required Education: M.Sc. in bioinformatics or biotechnology from recognized university or institute.
Job Location: Tura, Meghalaya, India

RNA-Seq Data Pathway and Gene-set Analysis Workflows

Jit — Fri, 25 Oct 2013 08:00:48 -0500

It describe the GAGE (Luo et al., 2009) /Pahview (Luo and Brouwer, 2013) workflows on RNA-Seq data pathway analysis and gene-set analysis. The gage package (2.12.0) now includes a new tutorial, “RNA-Seq Data Pathway and Gene-set Analysis Workflows“.

First cover a full workflow from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes. The same workflow can be used for GO analysis or other types of gene set analysis too. We also describe joint workflows, i.e. to do gene-level analysis using one of the major RNA-Seq analysis tools, DEseq/DEseq2, edgeR, limma and Cufflinks, and feed the results into GAGE/Pahview for pathway analysis or visualization. All these workflows are implemented in R/Bioconductor.

The work ows cover the most common situations and issues for RNA-Seq data pathway analysis. Issues like data quality assessment are relevant for data analysis in general yet out the scope of this tutorial. Although we focus on RNA-Seq data here, but pathway analysis work ow remains similar for microarray, particularly step 3-4 would be the same. Please check gage and pathview vigenttes for details.

Note: You need to update to current release versions of R(3.0.2)/ Bioconductor(2.13) to use all the features.

Reference:

Please check it out:
http://bioconductor.org/packages/release/bioc/html/gage.html
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

One page R survival guide !!

Rahul Nayak — Thu, 28 May 2015 21:10:12 -0500

There any many of the documents have been developed and tested by scientist around the world. I found this one really useful. The data used is available for download as data.zip.

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

Templates for the Data Scientist
1. A Template for Preparing Data: *OnePageR - *R
2. A Template for Building Models: *OnePageR - *R
Getting Started as a Data Scientist
1. Getting Started with R and Rattle: *Lecture - *Laboratory
2. Introducing and Interacting with R: *Lecture - *Laboratory
3. BasicR - OnePage(R) - Writing R scripts
Dealing With Data
1. Read Data into R: *OnePageR - *R
2. Explore and Summarise Data: *OnePageR - *R
3. Transform Data: *OnePageR - *R
4. Dealing with Dates and Time: (PDF, R) Dates and Time
5. Visualising Data with GGPlot2: *OnePageR - *R
6. Visualising Data with Maps *OnePageR - *R
7. Spatial (R) Spatial Analysis
8. Handling Big Data *OnePageR - *R
Descriptive Analytics
1. Cluster Analysis: *Lecture - *OnePageR - *R
2. Association Analysis: *Lecture
Predictive Analytics
1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
3. SVM (R)
4. KernLab (R)
5. NeuralNetworks (R)
6. NNet (R)
Model Delivery
1. Evaluating Models: *OnePageR - *R
2. Evaluation (R)
3. Scoring (R)
4. PMML (R) Exporting Models for Deployment
Advanced Topics
1. Text Mining: *OnePageR - *R
Advanced R Topics
1. Plots (PDF, R) Miscellaneous Plots
2. Functions (PDF, R) Writing Functions in R
3. Parallel (PDF, R) Parallel Execution
4. Packaging (R) Pulling it Together into a Package
5. Doing R with Style: *OnePageR - *R
6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *

RA WALK-IN-INTERVIEW @ NBFGR, Lucknow

Tue, 24 Feb 2015 08:23:48 -0600

F.No. 1(122)/2015-Admn. (CABin Project)
Research Associate/Young Professional/SRF Zoology job vacancies in National Bureau of Fish Genetic Resources (NBFGR)
Post Name: Research Associate (Computer Science/ Applications)
Qualification: Ph.D. In Computer Science/Computer Applications or equivalent. Or Post-Graduation in Computer Science/ Computer Applications with 1st Division or 60% marks or equivalent overall grade point average with at least two years of research experience. Desirable: 1. Expertise and experience of working/ handling High Performance Computing (H PC) and genomic resource data. 2. Expertise on database management, data mining technologies/ softwares/tools. 3. Published Research papers
No.of Post: 1
Pay Scale: Consolidated Rs.24,000/- p.m. + HRA (as admissible) for Ph.D. holders and consolidated `23,000/- + HRA (as admissible) for Master degree holder.
Age:40 years

Young Professional II (Computer Science/Applications)
Master degree in Computer Science/Computer Applications/B.Tech (Computer Science) or equivalent.
Desirable: 1. Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics/Data mining tools. 2. Experience in handling HPC, programming languages and database management packages.
A consolidated salary of Rs.25,000/- per month.
21 to 45 year

Young Professional II (Biotechnology/ Bioinformatics)
Master degree in Bioinformatics/ Biotechnology/ B. Tech(Biotech) or equivalent. Desirable: 1. Knowledge of Computational Genomics/Proteomics/Bioinformatics. 2. Expertise in NGS data analysis and knowledge of allied software and tools.
A consolidated salary of Rs.25,000/- per month.

Senior Research Fellow
1. Bachelors degree with Zoology, Fisheries and 2. Master's degree in Fishery science/ Zoology with Fisheries/ Biotechnology/ Life Sciences with specialization in Fisheries/ Molecular Biology. 3. 1 st Division or 60% marks or equivalent overall grade point average.
Desirable: Work experience in Fisheries, molecular research techniques, bioinformatics and Computer skills. NET qualified
Note: The project involves extensive exploration tours and sampling from water bodies all over India
Rs.16,000/- p.m. for 1st & 2nd year and `18,000/- p.m. for 3rd and subsequent years +HRA (as per rules) 35 years for male and 40 years for female candidate

How to apply

A walk-in-interview will be held on 04th March, 2015 at 10:00 hrs at National Bureau of Fish Genetic Resources, Lucknow. Eligible and desirous candidates fulfilling all the requirements may appear for the interview with duly filled in application giving full details of academic records and experience(s) along with attested photocopy as well as original copy of the relevant documents and a passport size photograph on the attached proforma.

http://www.nbfgr.res.in/

Import R Data

Abhimanyu Singh — Wed, 12 Jul 2017 08:30:46 -0500

It is often necessary to import sample textbook data into R before you start working on your homework.

Excel File

Quite frequently, the sample data is in Excel format, and needs to be imported into R prior to use. For this, we can use the function read.xls from the gdata package. It reads from an Excel spreadsheet and returns a data frame. The following shows how to load an Excel spreadsheet named "mydata.xls". This method requires Perl runtime to be present in the system.

> library(gdata)                   # load gdata package
> help(read.xls)                   # documentation
> mydata = read.xls("mydata.xls")  # read from first sheet

Alternatively, we can use the function loadWorkbook from the XLConnect package to read the entire workbook, and then load the worksheets with readWorksheet. The XLConnect package requires Java to be pre-installed.

> library(XLConnect) # load XLConnect package
> wk = loadWorkbook("mydata.xls")
> df = readWorksheet(wk, sheet="Sheet1")

Minitab File

If the data file is in Minitab Portable Worksheet format, it can be opened with the function read.mtp from the foreign package. It returns a list of components in the Minitab worksheet.

> library(foreign)                 # load the foreign package
> help(read.mtp)                   # documentation
> mydata = read.mtp("mydata.mtp")  # read from .mtp file

SPSS File

For the data files in SPSS format, it can be opened with the function read.spss also from the foreign package. There is a "to.data.frame" option for choosing whether a data frame is to be returned. By default, it returns a list of components instead.

> library(foreign) # load the foreign package
> help(read.spss) # documentation
> mydata = read.spss("myfile", to.data.frame=TRUE)

Table File

A data table can resides in a text file. The cells inside the table are separated by blank characters. Here is an example of a table with 4 rows and 3 columns.

100   a1   b1
200   a2   b2
300   a3   b3
400   a4   b4

Now copy and paste the table above in a file named "mydata.txt" with a text editor. Then load the data into the workspace with the function read.table.

> mydata = read.table("mydata.txt")  # read text file
> mydata                             # print data frame
   V1 V2 V3
1 100 a1 b1
2 200 a2 b2
3 300 a3 b3
4 400 a4 b4

For further detail of the function read.table, please consult the R documentation.

> help(read.table)

CSV File

The sample data can also be in comma separated values (CSV) format. Each cell inside such data file is separated by a special character, which usually is a comma, although other characters can be used as well.

The first row of the data file should contain the column names instead of the actual data. Here is a sample of the expected format.

Col1,Col2,Col3
100,a1,b1
200,a2,b2
300,a3,b3

After we copy and paste the data above in a file named "mydata.csv" with a text editor, we can read the data with the function read.csv.

> mydata = read.csv("mydata.csv")  # read csv file
> mydata
  Col1 Col2 Col3
1  100   a1   b1
2  200   a2   b2
3  300   a3   b3

In various European locales, as the comma character serves as the decimal point, the function read.csv2 should be used instead. For further detail of the read.csv and read.csv2 functions, please consult the R documentation.

> help(read.csv)

Working Directory

Finally, the code samples above assume the data files are located in the R working directory, which can be found with the function getwd.

> getwd() # get current working directory

You can select a different working directory with the function setwd(), and thus avoid entering the full path of the data files.

> setwd("") # set working directory

Note that the forward slash should be used as the path separator even on Windows platform.

> setwd("C:/MyDoc")