↓ Skip to main content

ClinQC: a tool for quality control and cleaning of Sanger and NGS data in clinical research

Overview of attention for article published in BMC Bioinformatics, February 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
116 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
ClinQC: a tool for quality control and cleaning of Sanger and NGS data in clinical research
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0915-y
Pubmed ID
Authors

Ram Vinay Pandey, Stephan Pabinger, Albert Kriegner, Andreas Weinhäusel

Abstract

Traditional Sanger sequencing has been used as a gold standard method for genetic testing in clinic to perform single gene test, which has been a cumbersome and expensive method to test several genes in heterogeneous disease such as cancer. With the advent of Next Generation Sequencing technologies, which produce data on unprecedented speed in a cost effective manner have overcome the limitation of Sanger sequencing. Therefore, for the efficient and affordable genetic testing, Next Generation Sequencing has been used as a complementary method with Sanger sequencing for disease causing mutation identification and confirmation in clinical research. However, in order to identify the potential disease causing mutations with great sensitivity and specificity it is essential to ensure high quality sequencing data. Therefore, integrated software tools are lacking which can analyze Sanger and NGS data together and eliminate platform specific sequencing errors, low quality reads and support the analysis of several sample/patients data set in a single run. We have developed ClinQC, a flexible and user-friendly pipeline for format conversion, quality control, trimming and filtering of raw sequencing data generated from Sanger sequencing and three NGS sequencing platforms including Illumina, 454 and Ion Torrent. First, ClinQC convert input read files from their native formats to a common FASTQ format and remove adapters, and PCR primers. Next, it split bar-coded samples, filter duplicates, contamination and low quality sequences and generates a QC report. ClinQC output high quality reads in FASTQ format with Sanger quality encoding, which can be directly used in down-stream analysis. It can analyze hundreds of sample/patients data in a single run and generate unified output files for both Sanger and NGS sequencing data. Our tool is expected to be very useful for quality control and format conversion of Sanger and NGS data to facilitate improved downstream analysis and mutation screening. ClinQC is a powerful and easy to handle pipeline for quality control and trimming in clinical research. ClinQC is written in Python with multiprocessing capability, run on all major operating systems and is available at https://sourceforge.net/projects/clinqc .

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Germany 1 <1%
Unknown 114 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 21%
Student > Master 23 20%
Student > Bachelor 17 15%
Student > Ph. D. Student 16 14%
Student > Doctoral Student 7 6%
Other 16 14%
Unknown 13 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 29%
Agricultural and Biological Sciences 29 25%
Computer Science 12 10%
Immunology and Microbiology 7 6%
Medicine and Dentistry 5 4%
Other 13 11%
Unknown 16 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2016.
All research outputs
#2,924,733
of 11,295,096 outputs
Outputs from BMC Bioinformatics
#1,406
of 4,195 outputs
Outputs of similar age
#97,862
of 345,386 outputs
Outputs of similar age from BMC Bioinformatics
#62
of 148 outputs
Altmetric has tracked 11,295,096 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 345,386 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.