metaBEETL: high-throughput analysis of heterogeneous microbial populations from shotgun DNA sequences

Ander, Christina; Schulz-Trieglaff, Ole; Stoye, Jens; Cox, A.J.

doi:10.1186/1471-2105-14-s5-s2

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…This allows fast matching of queries to the database by hashing algorithms, a concept that is often applied in the seeding step of seed-and-extend alignment-based methods. Other methods are based on similarities and dissimilarities of nucleotide composition ( 74 ), comparison of Burrows-Wheeler transformed sequence information ( 6 ), or machine learning approaches ( 156 ).…”

Section: Metataxonomic Tools and Databasesmentioning

confidence: 99%

A Robust Framework for Microbial Archaeology

Warinner

Herbig

Mann

et al. 2017

Annu. Rev. Genom. Hum. Genet.

159

179

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Microbial archaeology is flourishing in the era of high-throughput sequencing, revealing the agents behind devastating historical plagues, identifying the cryptic movements of pathogens in prehistory, and reconstructing the ancestral microbiota of humans. Here, we introduce the fundamental concepts and theoretical framework of the discipline, then discuss applied methodologies for pathogen identification and microbiome characterization from archaeological samples. We give special attention to the process of identifying, validating, and authenticating ancient microbes using high-throughput DNA sequencing data. Finally, we outline standards and precautions to guide future research in the field.

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Section: Metataxonomic Tools and Databasesmentioning

confidence: 99%

A Robust Framework for Microbial Archaeology

Warinner

Herbig

Mann

et al. 2017

Annu. Rev. Genom. Hum. Genet.

159

179

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“…They have had trouble with classifying reads of short length (<1000 base pairs), with Phymm being the first method published demonstrating reasonable accuracy at short read lengths [ 6 ]. Sequence similarity based methods, on the other hand, perform very well at identifying reads from genomes within the reference database that they search against, even at read lengths as short as 80 base pairs [ 7 ]. However, many reads from metagenomics samples come from genomes that are not in any reference database [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities

et al. 2015

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BackgroundThe field of metagenomics (study of genetic material recovered directly from an environment) has grown rapidly, with many bioinformatics analysis methods being developed. To ensure appropriate use of such methods, robust comparative evaluation of their accuracy and features is needed. For taxonomic classification of sequence reads, such evaluation should include use of clade exclusion, which better evaluates a method’s accuracy when identical sequences are not present in any reference database, as is common in metagenomic analysis. To date, relatively small evaluations have been performed, with evaluation approaches like clade exclusion limited to assessment of new methods by the authors of the given method. What is needed is a rigorous, independent comparison between multiple major methods, using the same in silico and in vitro test datasets, with and without approaches like clade exclusion, to better characterize accuracy under different conditions.ResultsAn overview of the features of 38 bioinformatics methods is provided, evaluating accuracy with a focus on 11 programs that have reference databases that can be modified and therefore most robustly evaluated with clade exclusion. Taxonomic classification of sequence reads was evaluated using both in silico and in vitro mock bacterial communities. Clade exclusion was used at taxonomic levels from species to class—identifying how well methods perform in progressively more difficult scenarios. A wide range of variability was found in the sensitivity, precision, overall accuracy, and computational demand for the programs evaluated. In experiments where distilled water was spiked with only 11 bacterial species, frequently dozens to hundreds of species were falsely predicted by the most popular programs. The different features of each method (forces predictions or not, etc.) are summarized, and additional analysis considerations discussed.ConclusionsThe accuracy of shotgun metagenomics classification methods varies widely. No one program clearly outperformed others in all evaluation scenarios; rather, the results illustrate the strengths of different methods for different purposes. Researchers must appreciate method differences, choosing the program best suited for their particular analysis to avoid very misleading results. Use of standardized datasets for method comparisons is encouraged, as is use of mock microbial community controls suitable for a particular metagenomic analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0788-5) contains supplementary material, which is available to authorized users.

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“…In [ 48 ] the eBWT was used to index reads from the 1000 Genomes Project [ 49 ] in order to support k -mer search queries. An eBWT-based compressed index of sets of reads has also been suggested as a basis for both RNA-Seq [ 50 ] and metagenomic [ 51 , 52 ] analyses.…”

Section: Introductionmentioning

confidence: 99%

Variable-order reference-free variant discovery with the Burrows-Wheeler Transform

et al. 2020

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Background In [Prezza et al., AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory consuming due to the use of additional heavy data structures (namely, the Suffix and LCP arrays), besides the BWT. Results In this paper, we introduce a new algorithm and the corresponding tool ebwt2InDel that (i) extend the framework of [Prezza et al., AMB 2019] to detect also INDELs, and (ii) implements recent algorithmic findings that allow to perform the whole analysis using just the BWT, thus reducing the working space by one order of magnitude and allowing the analysis of full genomes. Finally, we describe a simple strategy for effectively parallelizing our tool for SNP detection only. On a 24-cores machine, the parallel version of our tool is one order of magnitude faster than the sequential one. The tool ebwt2InDel is available at github.com/nicolaprezza/ebwt2InDel. Conclusions Results on a synthetic dataset covered at 30x (Human chromosome 1) show that our tool is indeed able to find up to 83% of the SNPs and 72% of the existing INDELs. These percentages considerably improve the 71% of SNPs and 51% of INDELs found by the state-of-the art tool based on de Bruijn graphs. We furthermore report results on larger (real) Human whole-genome sequencing experiments. Also in these cases, our tool exhibits a much higher sensitivity than the state-of-the art tool.

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metaBEETL: high-throughput analysis of heterogeneous microbial populations from shotgun DNA sequences

Cited by 15 publications

References 20 publications

A Robust Framework for Microbial Archaeology

A Robust Framework for Microbial Archaeology

Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities

Variable-order reference-free variant discovery with the Burrows-Wheeler Transform

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