T-REX17 is a transiently expressed non-coding RNA essential for human endoderm formation.

Long non-coding RNAs (lncRNAs) have emerged as fundamental regulators in various biological processes, including embryonic development and cellular differentiation. Despite much progress over the past decade, the genome-wide annotation of lncRNAs remains incomplete and many known non-coding loci are still poorly characterized. Here, we report the discovery of a previously unannotated lncRNA that is transcribed 230 kb upstream of the SOX17 gene and located within the same topologically associating domain. We termed it T-REX17 (Transcript Regulating Endoderm and activated by soX17) and show that it is induced following SOX17 activation but its expression is more tightly restricted to early definitive endoderm. Loss of T-REX17 affects crucial functions independent of SOX17 and leads to an aberrant endodermal transcriptome, signaling pathway deregulation and epithelial to mesenchymal transition defects. Consequently, cells lacking the lncRNA cannot further differentiate into more mature endodermal cell types. Taken together, our study identified and characterized T-REX17 as a transiently expressed and essential non-coding regulator in early human endoderm differentiation.

RLM: fast and simplified extraction of read-level methylation metrics from bisulfite sequencing data.

SUMMARY: Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzing single-read patterns. Over the past years, various metrics have been established to explore this layer of information. However, limited compatibility with alignment tools, reference genomes or the measurements they provide present a bottleneck for most groups to routinely perform read-level analysis. To address this, we developed RLM, a fast and scalable tool for the computation of several frequently used read-level methylation statistics. RLM supports standard alignment tools, works independently of the reference genome and handles most sequencing experiment designs. RLM can process large input files with a billion reads in just a few hours on common workstations. AVAILABILITY AND IMPLEMENTATION: https://github.com/sarahet/RLM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dnmt1 has de novo activity targeted to transposable elements.

DNA methylation plays a critical role during development, particularly in repressing retrotransposons. The mammalian methylation landscape is dependent on the combined activities of the canonical maintenance enzyme Dnmt1 and the de novo Dnmts, 3a and 3b. Here, we demonstrate that Dnmt1 displays de novo methylation activity in vitro and in vivo with specific retrotransposon targeting. We used whole-genome bisulfite and long-read Nanopore sequencing in genetically engineered methylation-depleted mouse embryonic stem cells to provide an in-depth assessment and quantification of this activity. Utilizing additional knockout lines and molecular characterization, we show that the de novo methylation activity of Dnmt1 depends on Uhrf1, and its genomic recruitment overlaps with regions that enrich for Uhrf1, Trim28 and H3K9 trimethylation. Our data demonstrate that Dnmt1 can catalyze DNA methylation in both a de novo and maintenance context, especially at retrotransposons, where this mechanism may provide additional stability for long-term repression and epigenetic propagation throughout development.

TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers.

Mammalian cells stably maintain high levels of DNA methylation despite expressing both positive (DNMT3A/B) and negative (TET1-3) regulators. Here, we analyzed the independent and combined effects of these regulators on the DNA methylation landscape using a panel of knockout human embryonic stem cell (ESC) lines. The greatest impact on global methylation levels was observed in DNMT3-deficient cells, including reproducible focal demethylation at thousands of normally methylated loci. Demethylation depends on TET expression and occurs only when both DNMT3s are absent. Dynamic loci are enriched for hydroxymethylcytosine and overlap with subsets of putative somatic enhancers that are methylated in ESCs and can be activated upon differentiation. We observe similar dynamics in mouse ESCs that were less frequent in epiblast stem cells (EpiSCs) and scarce in somatic tissues, suggesting a conserved pluripotency-linked mechanism. Taken together, our data reveal tightly regulated competition between DNMT3s and TETs at thousands of somatic regulatory sequences within pluripotent cells.

Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing.

Expansions of short tandem repeats are genetic variants that have been implicated in several neuropsychiatric and other disorders, but their assessment remains challenging with current polymerase-based methods1-4. Here we introduce a CRISPR-Cas-based enrichment strategy for nanopore sequencing combined with an algorithm for raw signal analysis. Our method, termed STRique for short tandem repeat identification, quantification and evaluation, integrates conventional sequence mapping of nanopore reads with raw signal alignment for the localization of repeat boundaries and a hidden Markov model-based repeat counting mechanism. We demonstrate the precise quantification of repeat numbers in conjunction with the determination of CpG methylation states in the repeat expansion and in adjacent regions at the single-molecule level without amplification. Our method enables the study of previously inaccessible genomic regions and their epigenetic marks.

Nanopype: a modular and scalable nanopore data processing pipeline.

SUMMARY: Long-read third-generation nanopore sequencing enables researchers to now address a range of questions that are difficult to tackle with short read approaches. The rapidly expanding user base and continuously increasing throughput have sparked the development of a growing number of specialized analysis tools. However, streamlined processing of nanopore datasets using reproducible and transparent workflows is still lacking. Here we present Nanopype, a nanopore data processing pipeline that integrates a diverse set of established bioinformatics software while maintaining consistent and standardized output formats. Seamless integration into compute cluster environments makes the framework suitable for high-throughput applications. As a result, Nanopype facilitates comparability of nanopore data analysis workflows and thereby should enhance the reproducibility of biological insights. AVAILABILITY AND IMPLEMENTATION: https://github.com/giesselmann/nanopype, https://nanopype.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.