DecontaMiner: A Pipeline for the Detection and Analysis of Contaminating Sequences in Human NGS Sequencing Data

Granata, Ilaria; Sangiovanni, Mara; Guarracino, Mario Rosario

doi:10.1007/978-3-319-45723-9_11

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…The DecontaMiner pipeline has been tested on two publicly available datasets downloaded from the GEO (Gene Expression Omnibus) portal. These datasets have also been used to test the first prototype as described in [43]. However, the pipeline has changed since then, and the NCBI databases of contaminant organisms as well.…”

Section: Resultsmentioning

confidence: 99%

“…Here we propose DecontaMiner, a tool developed to unravel the presence of contaminating sequences among the reads that fail to map to the reference genome. We described the first DecontaMiner prototype and analyzed the results in a previous work [43]. Here we present a more complete and mature version of the pipeline: the DecontaMiner’s code was completely reorganized in a way that permits to run all the processing steps separately.…”

Section: Introductionmentioning

confidence: 99%

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From trash to treasure: detecting unexpected contamination in unmapped NGS data

et al. 2019

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Background Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This ’misalignment’ can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream ’contamination’ from exogenous nucleic acids. Results Here we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach to identify bacteria, fungi and viruses genome contamination. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. DecontaMiner builds an offline HTML page containing summary statistics and plots. The latter are obtained using the state-of-the-art D3 javascript libraries. DecontaMiner has been mainly used to detect contamination in human RNA-Seq data. The software is freely available at http://www-labgtp.na.icar.cnr.it/decontaminer . Conclusions DecontaMiner is a tool designed and developed to investigate the presence of contaminating sequences in unmapped NGS data. It can suggest the presence of contaminating organisms in sequenced samples, that might derive either from laboratory contamination or from their biological source, and in both cases can be considered as worthy of further investigation and experimental validation. The novelty of DecontaMiner is mainly represented by its easy integration with the standard procedures of NGS data analysis, while providing a complete, reliable, and automatic pipeline. Electronic supplementary material The online version of this article (10.1186/s12859-019-2684-x) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From trash to treasure: detecting unexpected contamination in unmapped NGS data

et al. 2019

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“…Effort has been put into using already existing and/or creating new bioinformatics tools, especially for exploring pathogens in human sequence data [1, 6, 7]. In a study of the unmapped reads generated by the 1000 Genomes Project [8] biologically relevant information was identified from the reads that were non-human, such as human papilloma virus [9].…”

Section: Introductionmentioning

confidence: 99%

Exploring the unmapped DNA and RNA reads in a songbird genome

et al. 2019

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BackgroundA widely used approach in next-generation sequencing projects is the alignment of reads to a reference genome. Despite methodological and hardware improvements which have enhanced the efficiency and accuracy of alignments, a significant percentage of reads frequently remain unmapped. Usually, unmapped reads are discarded from the analysis process, but significant biological information and insights can be uncovered from these data. We explored the unmapped DNA (normal and bisulfite treated) and RNA sequence reads of the great tit (Parus major) reference genome individual. From the unmapped reads we generated de novo assemblies, after which the generated sequence contigs were aligned to the NCBI non-redundant nucleotide database using BLAST, identifying the closest known matching sequence.ResultsMany of the aligned contigs showed sequence similarity to different bird species and genes that were absent in the great tit reference assembly. Furthermore, there were also contigs that represented known P. major pathogenic species. Most interesting were several species of blood parasites such as Plasmodium and Trypanosoma.ConclusionsOur analyses revealed that meaningful biological information can be found when further exploring unmapped reads. For instance, it is possible to discover sequences that are either absent or misassembled in the reference genome, and sequences that indicate infection or sample contamination. In this study we also propose strategies to aid the capture and interpretation of this information from unmapped reads.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5378-2) contains supplementary material, which is available to authorized users.

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“…Effort has been put into using already existing and/or creating new bioinformatics tools, especially for exploring pathogens in human sequence data [1,6,7]. In a study of the unmapped reads generated by the 1000 Genomes Project [8] biologically relevant information was identified from the reads that were non-human such as e.g.…”

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confidence: 99%