Second Keynote Address: The Two Impeachments of Donald J. Trump

It was just two years ago this month that the Senate acquitted President Trump in the first impeachment trial, and one year ago this week, he was acquitted in the second trial for inciting an insurrection against the government of the United States. Now, in the first trial, Republican Senators voted to prohibit the trial managers, the actual prosecutors, from subpoenaing any witnesses or documents. They won because they were in the majority. Before both trials, enough Republican Senators, known as jurors, announced that they had already made up their minds to acquit the President—that the not-guilty verdicts were essentially rendered even before the trial started

GFAKluge: A C++ library and command line utilities for the Graphical Fragment Assembly formats

GFAKluge is a set of command line utilities and a C++ library for parsing and manipulating the Graphical Fragment Assembly (GFA) format. Genome assembly algorithms often use graph structures to represent relationships between reads during the assembly process. Such information is typically thrown away when assemblies are converted to FASTA files of contig sequences. Previous attempts to convey graph information did not gain widespread acceptance because there were no standard representations that were easily parsed and extensively used. The Graphical Fragment Assembly (GFA) format was proposed as a way to encode the graph structure of an assembly in a human-readable text format (Li, 2014). GFA aims to provide a single format for interchange between software for assembly, scaffolding, assessment and visualization. Such programs are often written in high-performance programming languages such as C or C++. GFAKluge facilitates interprogram exchange by providing a high-level C++ API for developers and a set of command line tools for users. We hope the availability of an open-source, easily extensible API will encourage software developers to consider adding support for GFA to their bioinformatics programs. Homepage: https://github.com/edawson/gfakluge License: MI

Vertebrate gene finding from multiple-species alignments using a two-level strategy

BACKGROUND: One way in which the accuracy of gene structure prediction in vertebrate DNA sequences can be improved is by analyzing alignments with multiple related species, since functional regions of genes tend to be more conserved. RESULTS: We describe DOGFISH, a vertebrate gene finder consisting of a cleanly separated site classifier and structure predictor. The classifier scores potential splice sites and other features, using sequence alignments between multiple vertebrate species, while the structure predictor hypothesizes coding transcripts by combining these scores using a simple model of gene structure. This also identifies and assigns confidence scores to possible additional exons. Performance is assessed on the ENCODE regions. We predict transcripts and exons across the whole human genome, and identify over 10,000 high confidence new coding exons not in the Ensembl gene set. CONCLUSION: We present a practical multiple species gene prediction method. Accuracy improves as additional species, up to at least eight, are introduced. The novel predictions of the whole-genome scan should support efficient experimental verification

Genome assembly in the telomere-to-telomere era

De novo assembly is the process of reconstructing the genome sequence of an organism from sequencing reads. Genome sequences are essential to biology, and assembly has been a central problem in bioinformatics for four decades. Until recently, genomes were typically assembled into fragments of a few megabases at best but technological advances in long-read sequencing now enable near complete chromosome-level assembly, also known as telomere-to-telomere assembly, for many organisms. Here we review recent progress on assembly algorithms and protocols. We focus on how to derive near telomere-to-telomere assemblies and discuss potential future developments

[X]uniqMAP: unique gene sequence regions in the human and mouse genomes

BACKGROUND: Current approaches for genome-wise functional analyses, such as microarray and RNA interference studies, rely on the specificity of oligonucleotide sequences to selectively target cellular transcripts. The design of specific oligos involves the determination of unique DNA regions in the gene/transcripts of interest from the targeted organism. This process is tedious, time consuming and it does not scale up for high-throughput studies. DESCRIPTION: Taking advantage of the availability of complete genome sequence information for mouse and human, the most widely used systems for the study of mammalian genetics, we have built a database, [X]uniqMAP, that stores the precalculated unique regions for all transcripts of these two organisms. For each gene, the database discriminates between those unique regions that are shared by all transcripts and those exclusive to single transcripts. In addition, it also provides those unique regions that are shared between orthologous genes from the two organisms. The database is updated regularly to reflect changes in genome assemblies and gene builds. CONCLUSION: Over 85% of genes have unique regions at least 19 bases long, with the majority being unique over 60% of their lengths. 14482 human genes share exactly at least a unique region with mouse genes, though such regions are typically under 40 bases long. The full data are publicly accessible online both interactively and for download. They should facilitate (i) the design of probes, primers and siRNAs for both small- and large-scale projects; and (ii) the identification of regions for the design of oligos that could be re-used to target equivalent gene/transcripts from human and mouse