Demo Data from EDBT 2026 / reviewers in the wild / expert
Sumitra Mohan
dblp:244/9093
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0009-0008-1702-1429ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
cancer genomics |
0.4 | 1 | 2019 | In silico error correction improves cfDNA mutation calling · Bioinform. 2019 |
Bioinformatics and computational biology › cancer genomics
circulating tumor DNA analysis |
0.4 | 1 | 2019 | In silico error correction improves cfDNA mutation calling · Bioinform. 2019 |
Bioinformatics and computational biology › sequence analysis
sequencing error correction |
0.1 | 1 | 2019 | In silico error correction improves cfDNA mutation calling · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
redundancy-based error correction · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | In silico error correction improves cfDNA mutation callingabstractMOTIVATION: Circulating-free DNA (cfDNA) profiling by sequencing is an important minimally invasive protocol for monitoring the mutation profile of solid tumours in cancer patients. Since the concentration of available cfDNA is limited, sample library generation relies on multiple rounds of PCR amplification, during which the accumulation of errors results in reduced sensitivity and lower accuracy. RESULTS: We present PCR Error Correction (PEC), an algorithm to identify and correct errors in short read sequencing data. It exploits the redundancy that arises from multiple rounds of PCR amplification. PEC is particularly well suited to applications such as single-cell sequencing and circulating tumour DNA (ctDNA) analysis, in which many cycles of PCR are used to generate sufficient DNA for sequencing from small amounts of starting material. When applied to ctDNA analysis, PEC significantly improves mutation calling accuracy, achieving similar levels of performance to more complex strategies that require additional protocol steps and access to calibration DNA datasets. AVAILABILITY AND IMPLEMENTATION: PEC is available under the GPL-v3 Open Source licence, and is freely available from: https://github.com/CRUKMI-ComputationalBiology/PCR_Error_Correction.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chang Sik Kim, Sumitra Mohan, Mahmood Ayub, Dominic G. Rothwell, Caroline Dive, Ged Brady, Crispin J. Miller |
Bioinform. | 2 |