Dataset Information

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.

ABSTRACT:

Background

Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs.

Results

We developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis. DiNAMO proves to compare favorably with other existing methods and is robust to noise.

Conclusions

We shown that DiNAMO software can serve as a tool to search for degenerate motifs in an exact manner using IUPAC models. DiNAMO can be used in scanning mode with sliding windows or in fixed position mode, which makes it suitable for numerous potential applications.

Availability

https://github.com/bonsai-team/DiNAMO .

SUBMITTER: Saad C

PROVIDER: S-EPMC5996464 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.

Saad Chadi C Noé Laurent L Richard Hugues H Leclerc Julie J Buisine Marie-Pierre MP Touzet Hélène H Figeac Martin M

BMC bioinformatics 20180611 1

<h4>Background</h4>Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Neverthele ...[more]

PMID: 29890948

Similar Datasets

Project description:BACKGROUND:DNA barcodes are short unique sequences used to label DNA or RNA-derived samples in multiplexed deep sequencing experiments. During the demultiplexing step, barcodes must be detected and their position identified. In some cases (e.g., with PacBio SMRT), the position of the barcode and DNA context is not well defined. Many reads start inside the genomic insert so that adjacent primers might be missed. The matter is further complicated by coincidental similarities between barcode sequences and reference DNA. Therefore, a robust strategy is required in order to detect barcoded reads and avoid a large number of false positives or negatives.For mass inference problems such as this one, false discovery rate (FDR) methods are powerful and balanced solutions. Since existing FDR methods cannot be applied to this particular problem, we present an adapted FDR method that is suitable for the detection of barcoded reads as well as suggest possible improvements. RESULTS:In our analysis, barcode sequences showed high rates of coincidental similarities with the Mus musculus reference DNA. This problem became more acute when the length of the barcode sequence decreased and the number of barcodes in the set increased. The method presented in this paper controls the tail area-based false discovery rate to distinguish between barcoded and unbarcoded reads. This method helps to establish the highest acceptable minimal distance between reads and barcode sequences. In a proof of concept experiment we correctly detected barcodes in 83% of the reads with a precision of 89%. Sensitivity improved to 99% at 99% precision when the adjacent primer sequence was incorporated in the analysis. The analysis was further improved using a paired end strategy. Following an analysis of the data for sequence variants induced in the Atp1a1 gene of C57BL/6 murine melanocytes by ultraviolet light and conferring resistance to ouabain, we found no evidence of cross-contamination of DNA material between samples. CONCLUSION:Our method offers a proper quantitative treatment of the problem of detecting barcoded reads in a noisy sequencing environment. It is based on the false discovery rate statistics that allows a proper trade-off between sensitivity and precision to be chosen.

Project description:BackgroundMosquito-borne infectious diseases pose a severe threat to public health in many areas of the world. Current methods for pathogen detection and surveillance are usually dependent on prior knowledge of the etiologic agents involved. Hence, efficient approaches are required for screening wild mosquito populations to detect known and unknown pathogens.Methodology/principal findingsIn this study, we explored the use of Next Generation Sequencing to identify viral agents in wild-caught mosquitoes. We extracted total RNA from different mosquito species from South China. Small 18-30 bp length RNA molecules were purified, reverse-transcribed into cDNA and sequenced using Illumina GAIIx instrumentation. Bioinformatic analyses to identify putative viral agents were conducted and the results confirmed by PCR. We identified a non-enveloped single-stranded DNA densovirus in the wild-caught Culex pipiens molestus mosquitoes. The majority of the viral transcripts (.>80% of the region) were covered by the small viral RNAs, with a few peaks of very high coverage obtained. The +/- strand sequence ratio of the small RNAs was approximately 7∶1, indicating that the molecules were mainly derived from the viral RNA transcripts. The small viral RNAs overlapped, enabling contig assembly of the viral genome sequence. We identified some small RNAs in the reverse repeat regions of the viral 5'- and 3' -untranslated regions where no transcripts were expected.Conclusions/significanceOur results demonstrate for the first time that high throughput sequencing of small RNA is feasible for identifying viral agents in wild-caught mosquitoes. Our results show that it is possible to detect DNA viruses by sequencing the small RNAs obtained from insects, although the underlying mechanism of small viral RNA biogenesis is unclear. Our data and those of other researchers show that high throughput small RNA sequencing can be used for pathogen surveillance in wild mosquito vectors.

Dataset Information

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.

Background

Results

Conclusions

Availability

Publications

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets