The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

High-performance medicine: the convergence of human and artificial intelligence

Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.

/pdf/improved-metagenomic-analysis-with-kraken-2-52i6sij3fo.pdf

Improved metagenomic analysis with Kraken 2.

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (
 https://github.com/marbl/mash
 ).

/pdf/mash-fast-genome-and-metagenome-distance-estimation-using-3b84vfnedh.pdf

Mash: fast genome and metagenome distance estimation using MinHash.

 Profiling of all domains of life. Marker and quasi-marker genes are now identified not only for microbes (Bacteria and Archaea), but also for viruses and Eukaryotic microbes (Fungi, Protozoa) that are crucial components of microbial communities.  A 6-fold increase in the number of considered species. Markers are now identified from >16,000 reference genomes and >7,000 unique species, dramatically expanding the comprehensiveness of the method. The new pipeline for identifying marker genes is also scalable to the quickly increasing number of reference genomes. See Supplementary Tables 1-3.  Introduction of the concept of quasi-markers, allowing more comprehensive and accurate profiling. For species with less than 200 markers, MetaPhlAn2 adopts additional quasi-marker sequences (Supplementary Note 2) that are occasionally present in other genomes (because of vertical conservation or horizontal transfer). At profiling time, if no other markers of the potentially confounding species are detected, the corresponding quasi-local markers are used to improve the quality and accuracy of the profiling.  Addition of strain-specific barcoding for microbial strain tracking. MetaPhlAn2 includes a completely new feature that exploits marker combinations to perform species-specific and genus-specific “barcoding” for strains in metagenomic samples (Supplementary Note 7). This feature can be used for culture-free pathogen tracking in epidemiology studies and strain tracking across microbiome samples. See Supplementary Figs. 12-20.  Strain-level identification for organisms with sequenced genomes. For the case in which a microbiome includes strains that are very close to one of those already sequenced, MetaPhlAn2 is now able to identify such strains and readily reports their abundances. See Supplementary Note 7, Supplementary Table 13, and Supplementary Fig. 21.  Improvement of false positive and false negative rates. Improvements in the underlying pipeline for identifying marker genes (including the increment of the adopted genomes and the use of quasi-markers) and the profiling procedure resulted in much improved quantitative performances (higher correlation with true abundances, lower false positive and false negative rates). See the validation on synthetic metagenomes in Supplementary Note 4.  Estimation of the percentage of reads mapped against known reference genomes. MetaPhlAn2 is now able to estimate the number of reads that would map against genomes of each clade detected as present and for which an estimation of its relative abundance is provided by the default output. See Supplementary Note 3 for details.  Integration of MetaPhlAn with post-processing and visualization tools. The MetaPhlAn2 package now includes a set of post-processing and visualization tools (“utils” subfolder of the MetaPhlAn2 repository). Multiple MetaPhlAn profiles can in fact be merged in an abundance table (“merge_metaphlan_tables.py”), exported as BIOM files, visualized as heatmap (“metaphlan_hclust_heatmap.py” or the integrated “hclust2” package), GraPhlAn plots (“export2graphlan.py” and the GraPhlAn package1), Krona2 plots (“metaphlan2krona.py”), and single microbe barplot across samples and conditions (“plot_bug.py”).

MetaPhlAn2 for enhanced metagenomic taxonomic profiling.

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce

/pdf/megan-community-edition-interactive-exploration-and-analysis-4sr8x52dk7.pdf

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

/pdf/kraken-ultrafast-metagenomic-sequence-classification-using-uhl9fkaqpz.pdf

Kraken: ultrafast metagenomic sequence classification using exact alignments

Although Kraken’s k-mer-based approach provides fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed five-fold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.

/pdf/improved-metagenomic-analysis-with-kraken-2-ucxhvns24s.pdf

Improved metagenomic analysis with Kraken 2

The raw data from a genome sequencing project sometimes contains DNA from contaminating organisms, which may be introduced during sample collection or sequence preparation. In some instances, these contaminants remain in the sequence even after assembly and deposition of the genome into public databases. As a result, searches of these databases may yield erroneous and confusing results. We used efficient microbiome analysis software to scan the draft assembly of domestic cow, Bos taurus, and identify 173 small contigs that appeared to derive from microbial contaminants. In the course of verifying these findings, we discovered that one genome, Neisseria gonorrhoeae TCDC-NG08107, although putatively a complete genome, contained multiple sequences that actually derived from the cow and sheep genomes. Our findings illustrate the need to carefully validate findings of anomalous DNA that rely on comparisons to either draft or finished genomes.

Unexpected cross-species contamination in genome sequencing projects

Background: The expression levels of bacterial genes can be measured directly using next-generation sequencing (NGS) methods, offering much greater sensitivity and accuracy than earlier, microarray-based methods. Most bioinformatics software for estimating levels of gene expression from NGS data has been designed for eukaryotic genomes, with algorithms focusing particularly on detection of splicing patterns. These methods do not perform well on bacterial genomes.

Results: Here we describe the first software system designed explicitly for quantifying the degree of gene expression in bacteria and other prokaryotes. EDGE-pro (Estimated Degree of Gene Expression in PROkaryotes) processes the raw data from an RNA-seq experiment on a bacterial or archaeal species and produces estimates of the expression levels for each gene in these gene-dense genomes.

Software: The EDGE-pro tool is implemented as a pipeline of C++ and Perl programs and is freely available as open-source code at http://www.genomics.jhu.edu/software/EDGE/index.shtml.

https://journals.sagepub.com/doi/pdf/10.4137/EBO.S11250

Derrick E. Wood

Papers

Kraken: ultrafast metagenomic sequence classification using exact alignments

Improved metagenomic analysis with Kraken 2.

Improved metagenomic analysis with Kraken 2

Unexpected cross-species contamination in genome sequencing projects

EDGE-pro: Estimated Degree of Gene Expression in Prokaryotic Genomes.