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Using shotgun sequence data to find active restriction enzyme genes.

ABSTRACT: Whole genome shotgun sequence analysis has become the standard method for beginning to determine a genome sequence. The preparation of the shotgun sequence clones is, in fact, a biological experiment. It determines which segments of the genome can be cloned into Escherichia coli and which cannot. By analyzing the complete set of sequences from such an experiment, it is possible to identify genes lethal to E. coli. Among this set are genes encoding restriction enzymes which, when active in E. coli, lead to cell death by cleaving the E. coli genome at the restriction enzyme recognition sites. By analyzing shotgun sequence data sets we show that this is a reliable method to detect active restriction enzyme genes in newly sequenced genomes, thereby facilitating functional annotation. Active restriction enzyme genes have been identified, and their activity demonstrated biochemically, in the sequenced genomes of Methanocaldococcus jannaschii, Bacillus cereus ATCC 10987 and Methylococcus capsulatus.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC2615612 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Using shotgun sequence data to find active restriction enzyme genes.

Zheng Yu Y Posfai Janos J Morgan Richard D RD Vincze Tamas T Roberts Richard J RJ

Nucleic acids research 20081106 1

Whole genome shotgun sequence analysis has become the standard method for beginning to determine a genome sequence. The preparation of the shotgun sequence clones is, in fact, a biological experiment. It determines which segments of the genome can be cloned into Escherichia coli and which cannot. By analyzing the complete set of sequences from such an experiment, it is possible to identify genes lethal to E. coli. Among this set are genes encoding restriction enzymes which, when active in E. col ...[more]

PMID: 18988632

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Project description:BACKGROUND: Expressed Sequence Tag (EST) sequences are widely used in applications such as genome annotation, gene discovery and gene expression studies. However, some of GenBank dbEST sequences have proven to be "unclean". Identification of cDNA termini/ends and their structures in raw ESTs not only facilitates data quality control and accurate delineation of transcription ends, but also furthers our understanding of the potential sources of data abnormalities/errors present in the wet-lab procedures for cDNA library construction. RESULTS: After analyzing a total of 309,976 raw Pinus taeda ESTs, we uncovered many distinct variations of cDNA termini, some of which prove to be good indicators of wet-lab artifacts, and characterized each raw EST by its cDNA terminus structure patterns. In contrast to the expected patterns, many ESTs displayed complex and/or abnormal patterns that represent potential wet-lab errors such as: a failure of one or both of the restriction enzymes to cut the plasmid vector; a failure of the restriction enzymes to cut the vector at the correct positions; the insertion of two cDNA inserts into a single vector; the insertion of multiple and/or concatenated adapters/linkers; the presence of 3'-end terminal structures in designated 5'-end sequences or vice versa; and so on. With a close examination of these artifacts, many problematic ESTs that have been deposited into public databases by conventional bioinformatics pipelines or tools could be cleaned or filtered by our methodology. We developed a software tool for Abnormality Filtering and Sequence Trimming for ESTs (AFST, http://code.google.com/p/afst/) using a pattern analysis approach. To compare AFST with other pipelines that submitted ESTs into dbEST, we reprocessed 230,783 Pinus taeda and 38,709 Arachis hypogaea GenBank ESTs. We found 7.4% of Pinus taeda and 29.2% of Arachis hypogaea GenBank ESTs are "unclean" or abnormal, all of which could be cleaned or filtered by AFST. CONCLUSIONS: cDNA terminal pattern analysis, as implemented in the AFST software tool, can be utilized to reveal wet-lab errors such as restriction enzyme cutting abnormities and chimeric EST sequences, detect various data abnormalities embedded in existing Sanger EST datasets, improve the accuracy of identifying and extracting bona fide cDNA inserts from raw ESTs, and therefore greatly benefit downstream EST-based applications.

Project description:There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140?000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.

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Using shotgun sequence data to find active restriction enzyme genes.

Publications

Using shotgun sequence data to find active restriction enzyme genes.

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