STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

Kalign 3: multiple sequence alignment of large datasets

Motivation Kalign is an efficient multiple sequence alignment (MSA) program capable of aligning thousands of protein or nucleotide sequences. However, current alignment problems involving large numbers of sequences are exceeding Kalign’s original design specifications. Here we present a completely re-written and updated version to meet current and future alignment challenges. Results Kalign now uses a SIMD (single instruction, multiple data) accelerated version of the bit-parallel Gene Myers algorithm to estimate pairwise distances, adopts a sequence embedding strategy and the bi-secting K-means algorithm to rapidly construct guide trees for thousands of sequences. The new version maintains high alignment accuracy on both protein and nucleotide alignments and scales better than other MSA tools. Availability and implementation The source code of Kalign and code to reproduce the results are found here: https://github.com/timolassmann/kalign. Contact [email protected]

Taxonomy annotation and guide tree errors in 16S rRNA databases

Sequencing of the 16S ribosomal RNA (rRNA) gene is widely used to survey microbial communities. Specialized 16S rRNA databases have been developed to support this approach including Greengenes, RDP and SILVA. Most taxonomy annotations in these databases are predictions from sequence rather than authoritative assignments based on studies of type strains or isolates. In this work, I investigated the taxonomy annotations and guide trees provided by these databases. Using a blinded test, I estimated that the annotation error rate of the RDP database is ∼10%. The branching orders of the Greengenes and SILVA guide trees were found to disagree at comparable rates with each other and with taxonomy annotations according to the training set (authoritative reference) provided by RDP, indicating that the trees have comparable quality. Pervasive conflicts between tree branching order and type strain taxonomies strongly suggest that the guide trees are unreliable guides to phylogeny. I found 249,490 identical sequences with conflicting annotations in SILVA v128 and Greengenes v13.5 at ranks up to phylum (7,804 conflicts), indicating that the annotation error rate in these databases is ∼17%.

StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees

Background Fast, accurate and high-throughput identification of bacterial isolates is in great demand. The present work was conducted to investigate the possibility of identifying isolates from unassembled next-generation sequencing reads using custom-made guide trees. Results A tool named StrainSeeker was developed that constructs a list of specific k-mers for each node of any given Newick-format tree and enables the identification of bacterial isolates in 1–2 min. It uses a novel algorithm, which analyses the observed and expected fractions of node-specific k-mers to test the presence of each node in the sample. This allows StrainSeeker to determine where the isolate branches off the guide tree and assign it to a clade whereas other tools assign each read to a reference genome. Using a dataset of 100 Escherichia coli isolates, we demonstrate that StrainSeeker can predict the clades of E. coli with 92% accuracy and correct tree branch assignment with 98% accuracy. Twenty-five thousand Illumina HiSeq reads are sufficient for identification of the strain. Conclusion StrainSeeker is a software program that identifies bacterial isolates by assigning them to nodes or leaves of a custom-made guide tree. StrainSeeker’s web interface and pre-computed guide trees are available at http://bioinfo.ut.ee/strainseeker. Source code is stored at GitHub: https://github.com/bioinfo-ut/StrainSeeker.

StrainSeeker: fast identification of bacterial strains from unassembled sequencing reads using user-provided guide trees.

Background Fast, accurate and high-throughput detection of bacteria is in great demand. The present work was conducted to investigate the possibility of identifying both known and unknown bacterial strains from unassembled next-generation sequencing reads using custom-made guide trees. Results A program named StrainSeeker was developed that constructs a list of specific k-mers for each node of any given Newick-format tree and enables rapid identification of bacterial genomes within minutes. StrainSeeker has been tested and shown to successfully identify Escherichia coli strains from mixed samples in less than 5 minutes. StrainSeeker can also identify bacterial strains from highly diverse metagenomics samples. StrainSeeker is available at http://bioinfo.ut.ee/strainseeker. Conclusions Our novel approach can be useful for both clinical diagnostics and research laboratories because novel bacterial strains are constantly emerging and their fast and accurate detection is very important.