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Accurately estimating the length distributions of genomic micro-satellites by tumor purity deconvolution
- Source :
- BMC Bioinformatics, BMC Bioinformatics, Vol 21, Iss S2, Pp 1-14 (2020)
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- Background Genomic micro-satellites are the genomic regions that consist of short and repetitive DNA motifs. Estimating the length distribution and state of a micro-satellite region is an important computational step in cancer sequencing data pipelines, which is suggested to facilitate the downstream analysis and clinical decision supporting. Although several state-of-the-art approaches have been proposed to identify micro-satellite instability (MSI) events, they are limited in dealing with regions longer than one read length. Moreover, based on our best knowledge, all of these approaches imply a hypothesis that the tumor purity of the sequenced samples is sufficiently high, which is inconsistent with the reality, leading the inferred length distribution to dilute the data signal and introducing the false positive errors. Results In this article, we proposed a computational approach, named ELMSI, which detected MSI events based on the next generation sequencing technology. ELMSI can estimate the specific length distributions and states of micro-satellite regions from a mixed tumor sample paired with a control one. It first estimated the purity of the tumor sample based on the read counts of the filtered SNVs loci. Then, the algorithm identified the length distributions and the states of short micro-satellites by adding the Maximum Likelihood Estimation (MLE) step to the existing algorithm. After that, ELMSI continued to infer the length distributions of long micro-satellites by incorporating a simplified Expectation Maximization (EM) algorithm with central limit theorem, and then used statistical tests to output the states of these micro-satellites. Based on our experimental results, ELMSI was able to handle micro-satellites with lengths ranging from shorter than one read length to 10kbps. Conclusions To verify the reliability of our algorithm, we first compared the ability of classifying the shorter micro-satellites from the mixed samples with the existing algorithm MSIsensor. Meanwhile, we varied the number of micro-satellite regions, the read length and the sequencing coverage to separately test the performance of ELMSI on estimating the longer ones from the mixed samples. ELMSI performed well on mixed samples, and thus ELMSI was of great value for improving the recognition effect of micro-satellite regions and supporting clinical decision supporting. The source codes have been uploaded and maintained at https://github.com/YixuanWang1120/ELMSI for academic use only.
- Subjects :
- Source code
Computer science
media_common.quotation_subject
Reliability (computer networking)
Locus (genetics)
Sample (statistics)
lcsh:Computer applications to medicine. Medical informatics
Polymorphism, Single Nucleotide
Biochemistry
DNA sequencing
User-Computer Interface
03 medical and health sciences
0302 clinical medicine
Structural Biology
Neoplasms
Sequencing data analysis
Cancer genomics
Humans
Repeated sequence
lcsh:QH301-705.5
Tumor purity
Molecular Biology
030304 developmental biology
media_common
0303 health sciences
Genome, Human
Research
Applied Mathematics
High-Throughput Nucleotide Sequencing
Computational pipeline
Genomic micro-satellite
Computer Science Applications
lcsh:Biology (General)
030220 oncology & carcinogenesis
lcsh:R858-859.7
Deconvolution
DNA microarray
Length distribution estimation
Algorithm
Algorithms
Microsatellite Repeats
Subjects
Details
- ISSN :
- 14712105
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....3fecd9212f052a460cee8f4cbcfbbbb7