I'm not sure about your specific query, but keep in mind that sequencing also has an error rate. So I wouldn't trust data from individual reads.
Yannick
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Evolutionary genomics research lab & Blast software
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Viral Genome reconstruction
Hi! I have some reads from sequencing a viral genome. We are analyzing readings with two matches, and we calculate the existing distance between both matches, for which we carry out histograms. I would like to know how can I distinguish if the regions that do not match (cuts) are due to chance or not?
It is about Avibirnavirus, it has its own polymerase called VP1. It seems that the areas that are not replicated are due to the fact that they are attached to another viral protein called VP3 , which has a protective action on the genome. VP3 requires a minimum size of 9bp to join.
Initially we used blast on the reads from the sequencing, and we are currently working with the reads that were discarded after using blast. From these discarded readings we are only using those that had two matches. We have calculated the distance between both matches and made histograms of these in which it is observed a density function with linear probability with a maximum in 0 and a minimum in the length of the genome.
However, as the multiplicity of infection of this virus increases, the distribution becomes irregular. For this reason, we think that it is possible that the reorganization of the genome after replication is carried out randomly.
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Differential Expression and Data Visualization: Recommended Tools for Next-Level Sequencing Analysis
After covering QC and alignment tools in the first segment and variant analysis and genome assembly in the second segment, we’re wrapping up with a discussion about tools for differential gene expression analysis and data visualization. In this article, we include recommendations from the following experts: Dr. Mark Ziemann, Senior Lecturer in Biotechnology and Bioinformatics, Deakin University; Dr. Medhat Mahmoud Postdoctoral Research Fellow at Baylor College of Medicine;...05-23-2023, 12:26 PM -
Continuing from our previous article, we share variant analysis and genome assembly tools recommended by our experts Dr. Medhat Mahmoud, Postdoctoral Research Fellow at Baylor College of Medicine, and Dr. Ming "Tommy" Tang, Director of Computational Biology at Immunitas and author of From Cell Line to Command Line.
Variant detection and analysis tools
Mahmoud classifies variant detection work into two main groups: short variants (<50...05-19-2023, 10:03 AM -
With new tools and computational resources being released regularly, it can be hard to determine which are best suited for the analysis process and which older tools continue to be maintained. In an effort to assist the sequencing community, we interviewed three highly skilled bioinformaticians about their recommended tools for several important analysis applications.
Quality control and preprocessing tools
“Garbage in, garbage out” is a popular...05-16-2023, 10:11 AM
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