Dataset Information

Protein-mediated error correction for de novo DNA synthesis.

ABSTRACT: The availability of inexpensive, on demand synthetic DNA has enabled numerous powerful applications in biotechnology, in turn driving considerable present interest in the de novo synthesis of increasingly longer DNA constructs. The synthesis of DNA from oligonucleotides into products even as large as small viral genomes has been accomplished. Despite such achievements, the costs and time required to generate such long constructs has, to date, precluded gene-length (and longer) DNA synthesis from being an everyday research tool in the same manner as PCR and DNA sequencing. A critical barrier to low-cost, high-throughput de novo DNA synthesis is the frequency at which errors pervade the final product. Here, we employ a DNA mismatch-binding protein, MutS (from Thermus aquaticus) to remove failure products from synthetic genes. This method reduced errors by >15-fold relative to conventional gene synthesis techniques, yielding DNA with one error per 10 000 base pairs. The approach is general, scalable and can be iterated multiple times for greater fidelity. Reductions in both costs and time required are demonstrated for the synthesis of a 2.5 kb gene.

SUBMITTER: Carr PA

PROVIDER: S-EPMC534640 | biostudies-literature | 2004 Nov

REPOSITORIES: biostudies-literature

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Protein-mediated error correction for de novo DNA synthesis.

Carr Peter A PA Park Jason S JS Lee Yoon-Jae YJ Yu Tiffany T Zhang Shuguang S Jacobson Joseph M JM

Nucleic acids research 20041123 20

The availability of inexpensive, on demand synthetic DNA has enabled numerous powerful applications in biotechnology, in turn driving considerable present interest in the de novo synthesis of increasingly longer DNA constructs. The synthesis of DNA from oligonucleotides into products even as large as small viral genomes has been accomplished. Despite such achievements, the costs and time required to generate such long constructs has, to date, precluded gene-length (and longer) DNA synthesis from ...[more]

PMID: 15561997

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Project description:BackgroundSeveral standalone error correction tools have been proposed to correct sequencing errors in Illumina data in order to facilitate de novo genome assembly. However, in a recent survey, we showed that state-of-the-art assemblers often did not benefit from this pre-correction step. We found that many error correction tools introduce new errors in reads that overlap highly repetitive DNA regions such as low-complexity patterns or short homopolymers, ultimately leading to a more fragmented assembly.ResultsWe propose BrownieCorrector, an error correction tool for Illumina sequencing data that focuses on the correction of only those reads that overlap short DNA patterns that are highly repetitive in the genome. BrownieCorrector extracts all reads that contain such a pattern and clusters them into different groups using a community detection algorithm that takes into account both the sequence similarity between overlapping reads and their respective paired-end reads. Each cluster holds reads that originate from the same genomic region and hence each cluster can be corrected individually, thus providing a consistent correction for all reads within that cluster.ConclusionsBrownieCorrector is benchmarked using six real Illumina datasets for different eukaryotic genomes. The prior use of BrownieCorrector improves assembly results over the use of uncorrected reads in all cases. In comparison with other error correction tools, BrownieCorrector leads to the best assembly results in most cases even though less than 2% of the reads within a dataset are corrected. Additionally, we investigate the impact of error correction on hybrid assembly where the corrected Illumina reads are supplemented with PacBio data. Our results confirm that BrownieCorrector improves the quality of hybrid genome assembly as well. BrownieCorrector is written in standard C++11 and released under GPL license. BrownieCorrector relies on multithreading to take advantage of multi-core/multi-CPU systems. The source code is available at https://github.com/biointec/browniecorrector .

Project description:Autophagy is an intracellular degradation mechanism in response to nutrient starvation. Via autophagy, some nonessential cellular constituents are degraded in a lysosome-dependent manner to generate biomolecules that can be utilized for maintaining the metabolic homeostasis. Although it is known that under starvation the global protein synthesis is significantly reduced mainly due to suppression of MTOR (mechanistic target of rapamycin serine/threonine kinase), emerging evidence demonstrates that de novo protein synthesis is involved in the autophagic process. However, characterizing these de novo proteins has been an issue with current techniques. Here, we developed a novel method to identify newly synthesized proteins during starvation-mediated autophagy by combining bio-orthogonal noncanonical amino acid tagging (BONCAT) and isobaric tags for relative and absolute quantitation (iTRAQTM). Using bio-orthogonal metabolic tagging, L-azidohomoalanine (AHA) was incorporated into newly synthesized proteins which were then enriched with avidin beads after a click reaction between alkyne-bearing biotin and AHA's bio-orthogonal azide moiety. The enriched proteins were subjected to iTRAQ labeling for protein identification and quantification using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Via the above approach, we identified and quantified a total of 1176 proteins and among them 711 proteins were found to meet our defined criteria as de novo synthesized proteins during starvation-mediated autophagy. The characterized functional profiles of the 711 newly synthesized proteins by bioinformatics analysis suggest their roles in ensuring the prosurvival outcome of autophagy. Finally, we performed validation assays for some selected proteins and found that knockdown of some genes has a significant impact on starvation-induced autophagy. Thus, we think that the BONCAT-iTRAQ approach is effective in the identification of newly synthesized proteins and provides useful insights to the molecular mechanisms and biological functions of autophagy.

Project description:BACKGROUND:The challenges when developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize a de Bruijn graph, with which complexity grows linearly with data size while suffering from errors and repeats. Although one can correct the errors by inspecting the topological structure of the graph, this is not an easy task when there are too many branches. Two research directions are to improve either the graph reliability or the path search precision, and in this study, we focused on the former. RESULTS:We present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. In the proposed approach, we built contigs by direct read alignment within a distance margin and performed a junction search to construct splicing graphs. While doing so, a contig of length l was represented by a 4 × l matrix (called a consensus matrix), in which each element was the base count of the aligned reads so far. A representative sequence was obtained by taking the majority in each column of the consensus matrix to be used for further read alignment. Once the splicing graphs had been obtained, we used IsoLasso to find paths with a noticeable read depth. The experiments using real and simulated reads show that the method provided considerable improvement in sensitivity and moderately better performance when comparing sensitivity and precision. This was achieved by the error-aware graph construction using the consensus matrix, with which the reads having errors were made usable for the graph construction (otherwise, they might have been eventually discarded). This improved the quality of the coverage depth information used in the subsequent path search step and finally the reliability of the graph. CONCLUSIONS:De novo assembly is mainly used to explore undiscovered isoforms and must be able to represent as many reads as possible in an efficient way. In this sense, TraRECo provides us with a potential alternative for improving graph reliability even though the computational burden is much higher than the single k-mer in the de Bruijn graph approach.

Dataset Information

Protein-mediated error correction for de novo DNA synthesis.

Publications

Protein-mediated error correction for de novo DNA synthesis.

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