Length biases in single-cell RNA sequencing of pre-mRNA
Single-cell RNA sequencing data can be modeled using Markov chains to yield genome-wide insights into transcriptional physics. However, quantitative inference with such data requires careful assessment of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data....
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
|
| Series: | Biophysical Reports |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667074722000544 |
| _version_ | 1797955445471576064 |
|---|---|
| author | Gennady Gorin Lior Pachter |
| author_facet | Gennady Gorin Lior Pachter |
| author_sort | Gennady Gorin |
| collection | DOAJ |
| description | Single-cell RNA sequencing data can be modeled using Markov chains to yield genome-wide insights into transcriptional physics. However, quantitative inference with such data requires careful assessment of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data. To explain this trend, we propose a length-based model of capture bias, which may produce false-positive observations. We solve this model and use it to find concordant parameter trends as well as systematic, mechanistically interpretable technical and biological differences in paired data sets. |
| first_indexed | 2024-04-10T23:33:16Z |
| format | Article |
| id | doaj.art-c80b90a16f274bfa9f0f58977cb70063 |
| institution | Directory Open Access Journal |
| issn | 2667-0747 |
| language | English |
| last_indexed | 2024-04-10T23:33:16Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biophysical Reports |
| spelling | doaj.art-c80b90a16f274bfa9f0f58977cb700632023-01-12T04:19:57ZengElsevierBiophysical Reports2667-07472023-03-0131100097Length biases in single-cell RNA sequencing of pre-mRNAGennady Gorin0Lior Pachter1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CaliforniaDivision of Biology and Biological Engineering, Pasadena, California; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California; Corresponding authorSingle-cell RNA sequencing data can be modeled using Markov chains to yield genome-wide insights into transcriptional physics. However, quantitative inference with such data requires careful assessment of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data. To explain this trend, we propose a length-based model of capture bias, which may produce false-positive observations. We solve this model and use it to find concordant parameter trends as well as systematic, mechanistically interpretable technical and biological differences in paired data sets.http://www.sciencedirect.com/science/article/pii/S2667074722000544 |
| spellingShingle | Gennady Gorin Lior Pachter Length biases in single-cell RNA sequencing of pre-mRNA Biophysical Reports |
| title | Length biases in single-cell RNA sequencing of pre-mRNA |
| title_full | Length biases in single-cell RNA sequencing of pre-mRNA |
| title_fullStr | Length biases in single-cell RNA sequencing of pre-mRNA |
| title_full_unstemmed | Length biases in single-cell RNA sequencing of pre-mRNA |
| title_short | Length biases in single-cell RNA sequencing of pre-mRNA |
| title_sort | length biases in single cell rna sequencing of pre mrna |
| url | http://www.sciencedirect.com/science/article/pii/S2667074722000544 |
| work_keys_str_mv | AT gennadygorin lengthbiasesinsinglecellrnasequencingofpremrna AT liorpachter lengthbiasesinsinglecellrnasequencingofpremrna |