Two yeast/E. coli shuttle vectors have been constructed. The two vectors, YEp351 and YEp352, have the following properties: (1) they can replicate autonomously in Saccharomyces cerevisiae and in E. coli; (2) they contain the beta-lactamase gene and confer ampicillin resistance to E. coli; (3) they contain the entire sequence of pUC18; (4) all ten restriction sites of the multiple cloning region of pUC18 including EcoRI, SacI, KpnI, SmaI, BamHI, XbaI, SalI, PstI, SphI and HindIII are unique in YEp352; these sites are also unique in YEp351 except for EcoRI and KpnI, which occur twice; (5) recombinant plasmids with DNA inserts in the multiple cloning region of YEp351 and YEp352 can be recognised by loss of beta-galactosidase function in appropriate E. coli hosts; (6) YEp351 and YEp352 contain the yeast LEU2 and URA3 genes, respectively, allowing for selection of these auxotrophic markers in yeast and E. coli; (7) both plasmids are retained with high frequency in yeast grown under non-selective conditions indicative of high plasmid copy number. The above properties make the shuttle vectors suitable for construction of yeast genomic libraries and for cloning of DNA fragments defined by a large number of different restriction sites. The two vectors have been further modified by deletion of the sequences necessary for autonomous replication in yeast. The derivative plasmids YIp351 and YIp352 can therefore be used to integrate specific sequences into yeast chromosomal DNA.

Perspective │2 Artificial intelligence (AI) tools are increasingly being applied in drug discovery. Whilst some protagonists point to vast opportunities potentially offered by such tools, others remain skeptical, waiting for a clear impact to be shown in drug discovery projects. The truth is probably somewhere between these extremes, but it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' for small-molecule drug discovery with AI and approaches to address them.

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Forward genetic screens in model organisms are vital for identifying novel genes essential for developmental or disease processes. One drawback of these screens is the labor-intensive and sometimes inconclusive process of mapping the causative mutation. To leverage high-throughput techniques to improve this mapping process, we have developed a Mutation Mapping Analysis Pipeline for Pooled RNA-seq (MMAPPR) that works without parental strain information or requiring a preexisting SNP map of the organism, and adapts to differential recombination frequencies across the genome. MMAPPR accommodates the considerable amount of noise in RNA-seq data sets, calculates allelic frequency by Euclidean distance followed by Loess regression analysis, identifies the region where the mutation lies, and generates a list of putative coding region mutations in the linked genomic segment. MMAPPR can exploit RNA-seq data sets from isolated tissues or whole organisms that are used for gene expression and transcriptome analysis in novel mutants. We tested MMAPPR on two known mutant lines in zebrafish, nkx2.5 and tbx1, and used it to map two novel ENU-induced cardiovascular mutants, with mutations found in the ctr9 and cds2 genes. MMAPPR can be directly applied to other model organisms, such as Drosophila and Caenorhabditis elegans, that are amenable to both forward genetic screens and pooled RNA-seq experiments. Thus, MMAPPR is a rapid, cost-efficient, and highly automated pipeline, available to perform mutant mapping in any organism with a well-assembled genome.

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