Ubc13 is an E2 ubiquitin conjugating enzyme that functions in nuclear DNA damage signaling and cytoplasmic NF-κB signaling. Here we present the structures of complexes of Ubc13 with two inhibitors, NSC697923 and BAY 11-7082, which inhibit DNA damage and NF-κB signaling in human cells. NSC697923 and BAY 11-7082 both inhibit Ubc13 by covalent adduct formation through a Michael addition at the Ubc13 active site cysteine. The resulting adducts of both compounds exploit a binding groove unique to Ubc13. We developed a Ubc13 mutant which resists NSC697923 inhibition and, using this mutant, we show that the inhibition of cellular DNA damage and NF-κB signaling by NSC697923 is largely due to specific Ubc13 inhibition. We propose that unique structural features near the Ubc13 active site could provide a basis for the rational development and design of specific Ubc13 inhibitors.

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Paired end DNA sequencing provides additional information about the sequence data that is used in sequence assembly, mapping, and other downstream bioinformatics analysis. Paired end reads are usually provided as two fastq-format files, with each file representing one end of the read. Many commonly used downstream tools require that the sequence reads appear in each file in the same order, and reads that do not have a pair in the corresponding file are placed in a separate file of singletons. Although most sequencing instruments capable of generating paired end reads produce files where each read has a corresponding mate, many downstream bioinformatics manipulations break the one-to-one correspondence between reads, and paired-end sequence files loose synchronicity, and contain either unordered sequences or sequences in one or other file without a mate. Trivial solutions to this problem require reading one or both of the DNA sequence files into memory but quickly become limited by computational resources for moderate to large sized sequence files that are common nowadays. Here, we introduce a fast and memory efficient solution, written in C for portability, that synchronizes paired-end fastq files for subsequent analysis and places unmatched reads into singleton files.Fastq-pair is freely available from https://github.com/linsalrob/fastq-pair and is released under the MIT license. fastq-pairFastq-format files have become the dominant file format for sharing DNA sequences as they conveniently contain both the sequence and quality information in a single file (1) . In addition, paired end sequencing has come to dominate sequencing approaches because of the additional information gained from knowing the distance between the pairs and potentially the longer sequences that can be acquired from by joining the overlapping pairs. For example, the sequence read archive contains twice as many paired end libraries as single read libraries (As of Feb 1st 2018 there were 2,233,015 paired end libraries and 1,110,884 single read libraries in the SRA).Many downstream tools that are used to join paired end read data (e.g. pear (2) ), assemble DNA sequences (e.g. spades or meta-spades (3) ), map reads to reference sequences (e.g. bowtie2 (4) ), require that the paired sequences be synchronized. In particular these tools require that the two files that represent a single paired end sequencing run (i) have the same number of reads in each file and (ii) that the left and right (or forward and reverse, depending on your preferred terminology) sequences appear in the same order in each file. In contrast, several upstream applications do not provide paired-end sequences in synchronized files. For 1 example, fastq-dump that is widely used to retrieve sequences from the sequence read archive (SRA) does not automatically synchronize the order of sequences in files and several trimming programs result in unordered paired end reads, although an undocumented option (--split-3) maybe appended to the fastq-dump command to request the sequences ...

Viruses of prokaryotes are extremely abundant and diverse. Culture-independent approaches have recently shed light on the biodiversity these biological entities. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), based on the analysis of nearly 200,000 viral genomes. RaFAH outperformed other methods for virus-host prediction (F1-score = 0.97 at the level of phylum). RaFAH was applied to diverse datasets encompassing genomes of uncultured viruses derived from eight different biomes of medical, biotechnological, and environmental relevance, and was capable of accurately describing these viromes. This led to the discovery of 537 genomic sequences of archaeal viruses. These viruses represent previously unknown lineages and their genomes encode novel auxiliary metabolic genes, which shed light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.