In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

Summary: Analyzing the functional profile of a microbial community from unannotated shotgun sequencing reads is one of the important goals in metagenomics. Functional profiling has valuable applications in biological research because it identifies the abundances of the functional genes of the organisms present in the original sample, answering the question what they can do. Currently, available tools do not scale well with increasing data volumes, which is important because both the number and lengths of the reads produced by sequencing platforms keep increasing. Here, we introduce SUPER-FOCUS, SUbsystems Profile by databasE Reduction using FOCUS, an agile homology-based approach using a reduced reference database to report the subsystems present in metagenomic datasets and profile their abundances. SUPER-FOCUS was tested with over 70 real metagenomes, the results showing that it accurately predicts the subsystems present in the profiled microbial communities, and is up to 1000 times faster than other tools.Availability and implementation: SUPER-FOCUS was implemented in Python, and its source code and the tool website are freely available at https://edwards.sdsu.edu/SUPERFOCUS.Contact: redwards@mail.sdsu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

One of the major goals in metagenomics is to identify the organisms present in a microbial community from unannotated shotgun sequencing reads. Taxonomic profiling has valuable applications in biological and medical research, including disease diagnostics. Most currently available approaches do not scale well with increasing data volumes, which is important because both the number and lengths of the reads provided by sequencing platforms keep increasing. Here we introduce FOCUS, an agile composition based approach using non-negative least squares (NNLS) to report the organisms present in metagenomic samples and profile their abundances. FOCUS was tested with simulated and real metagenomes, and the results show that our approach accurately predicts the organisms present in microbial communities. FOCUS was implemented in Python. The source code and web-sever are freely available at .

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.. CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a

Genome sequencing has become routine, however genome assembly still remains a challenge despite the computational advances in the last decade. In particular, the abundance of repeat elements in genomes makes it difficult to assemble them into a single complete sequence. Identical repeats shorter than the average read length can generally be assembled without issue. However, longer repeats such as ribosomal RNA operons cannot be accurately assembled using existing tools. The application Scaffold_builder was designed to generate scaffolds – super contigs of sequences joined by N-bases – based on the similarity to a closely related reference sequence. This is independent of mate-pair information and can be used complementarily for genome assembly, e.g. when mate-pairs are not available or have already been exploited. Scaffold_builder was evaluated using simulated pyrosequencing reads of the bacterial genomes Escherichia coli 042, Lactobacillus salivarius UCC118 and Salmonella enterica subsp. enterica serovar Typhi str. P-stx-12. Moreover, we sequenced two genomes from Salmonella enterica serovar Typhimurium LT2 G455 and Salmonella enterica serovar Typhimurium SDT1291 and show that Scaffold_builder decreases the number of contig sequences by 53% while more than doubling their average length. Scaffold_builder is written in Python and is available at http://edwards.sdsu.edu/scaffold_builder. A web-based implementation is additionally provided to allow users to submit a reference genome and a set of contigs to be scaffolded.