DNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF)—a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. Nucleotide Archival Format compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli and zstd. NAF compressor and decompressor, as well as format specification are available at https://github.com/KirillKryukov/naf. Format specification is in public domain. Compressor and decompressor are open source under the zlib/libpng license, free for nearly any use. Supplementary data are available at Bioinformatics online.

Background: Nearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for a more efficient compression tool. Although numerous compressors exist, both specialized and general-purpose, choosing one of them was difficult because no comprehensive analysis of their comparative advantages for sequence compression was available. Findings: We systematically benchmarked 430 settings of 48 compressors (including 29 specialized sequence compressors and 19 general-purpose compressors) on representative FASTA-formatted datasets of DNA, RNA, and protein sequences. Each compressor was evaluated on 17 performance measures, including compression strength, as well as time and memory required for compression and decompression. We used 27 test datasets including individual genomes of various sizes, DNA and RNA datasets, and standard protein datasets. We summarized the results as the Sequence Compression Benchmark database (SCB database, http://kirr.dyndns.org/sequence-compression-benchmark/), which allows custom visualizations to be built for selected subsets of benchmark results. Conclusion: We found that modern compressors offer a large improvement in compactness and speed compared to gzip. Our benchmark allows compressors and their settings to be compared using a variety of performance measures, offering the opportunity to select the optimal compressor on the basis of the data type and usage scenario specific to a particular application.

Invariant natural killer T (iNKT) cells are a group of innate-like T lymphocytes that recognize lipid antigens. They are supposed to be tissue resident and important for systemic and local immune regulation. To investigate the heterogeneity of iNKT cells, we recharacterized iNKT cells in the thymus and peripheral tissues. iNKT cells in the thymus were divided into three subpopulations by the expression of the natural killer cell receptor CD244 and the chemokine receptor CXCR6 and designated as C0 (CD244 ⁻ CXCR6 ⁻ ), C1 (CD244 ⁻ CXCR6 ⁺ ), or C2 (CD244 ⁺ CXCR6 ⁺ ) iNKT cells. The development and maturation of C2 iNKT cells from C0 iNKT cells strictly depended on IL-15 produced by thymic epithelial cells. C2 iNKT cells expressed high levels of IFN-γ and granzymes and exhibited more NK cell–like features, whereas C1 iNKT cells showed more T cell–like characteristics. C2 iNKT cells were influenced by the microbiome and aging and suppressed the expression of the autoimmune regulator AIRE in the thymus. In peripheral tissues, C2 iNKT cells were circulating that were distinct from conventional tissue-resident C1 iNKT cells. Functionally, C2 iNKT cells protected mice from the tumor metastasis of melanoma cells by enhancing antitumor immunity and promoted antiviral immune responses against influenza virus infection. Furthermore, we identified human CD244 ⁺ CXCR6 ⁺ iNKT cells with high cytotoxic properties as a counterpart of mouse C2 iNKT cells. Thus, this study reveals a circulating subset of iNKT cells with NK cell–like properties distinct from conventional tissue-resident iNKT cells.

Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image–based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.

Background: It has been suggested that the local microbiota in the reproductive organs is relevant to women's health and may also affect pregnancy outcomes. Analysis of partial 16S ribosomal RNA (rRNA) gene sequences generated by short-read sequencers has been used to identify vaginal and endometrial microbiota, but it requires a long time to obtain the results, making it unsuitable for rapid bacterial identification from a small specimen amount in a clinical context. Methods: We developed a simple workflow using the nanopore sequencer MinION that allows high-resolution and rapid differentiation of vaginal microbiota. Vaginal samples collected from 18 participants were subjected to DNA extraction and full-length 16S rRNA gene sequencing with MinION. Results: The principal coordinate analysis showed no differences in the bacterial compositions regardless of the sample collection method. The analysis of vaginal microbiota could be completed with a total analysis time of approximately four hours, allowing same-day results. Taxonomic profiling by MinION sequencing revealed relatively low diversity of the vaginal bacterial community, identifying the prevailing Lactobacillus species and several causative agents of bacterial vaginosis. Conclusions: Full-length 16S rRNA gene sequencing analysis with MinION provides a rapid means for identifying vaginal bacteria with higher resolution. Species-level profiling of human vaginal microbiota by MinION sequencing can allow the analysis of associations with conditions such as genital infections, endometritis, and threatened miscarriage.

Although a microbiological diagnosis of pleural infection is clinically important, it is often complicated by prior antibiotic treatment and/or difficulties with culturing some bacterial species. T...