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Abundant cellular transcripts occupy most of the sequencing reads in the transcriptome, making it challenging to assay for low-abundant transcripts. Here, we utilize the adaptive sampling function of Oxford Nanopore sequencing to selectively deplete and enrich RNAs of interest without biochemical manipulation before sequencing. Adaptive sampling performed on a pool of in vitro transcribed RNAs resulted in a net increase of 22-30% in the proportion of transcripts of interest in the population. Enriching and depleting different proportions of the Candida albicans transcriptome also resulted in a 11-13.5% increase in the number of reads on target transcripts, with longer and more abundant transcripts being more efficiently depleted. Depleting all currently annotated Candida albicans transcripts did not result in an absolute enrichment of remaining transcripts, although we identified 26 previously unknown transcripts and isoforms, 17 of which are antisense to existing transcripts. Further improvements in the adaptive sampling of RNAs will allow the technology to be widely applied to study RNAs of interest in diverse transcriptomes. It can be difficult to find rare transcripts when sequencing a transcriptome. Here the authors show adaptive sampling on direct RNA runs to increase the likelihood of finding less frequent ones while selectively ejecting the higher-abundance transcripts.

Telomeres are specialized nucleoprotein structures at the ends of linear chromosomes. The progressive shortening of steady-state telomere length in normal human somatic cells is a promising biomarker for age-associated diseases. However, there remain substantial challenges in quantifying telomere length due to the lack of high-throughput method with nucleotide resolution for individual telomere. Here, we describe a workflow to capture telomeres using newly designed telobaits in human culture cell lines as well as clinical patient samples and measure their length accurately at nucleotide resolution using single-molecule real-time (SMRT) sequencing. Our results also reveal the extreme heterogeneity of telomeric variant sequences (TVSs) that are dispersed throughout the telomere repeat region. The presence of TVSs disrupts the continuity of the canonical (5’-TTAGGG-3’)n telomere repeats, which affects the binding of shelterin complexes at the chromosomal ends and telomere protection. These findings may have profound implications in human aging and diseases. There exist challenges in quantifying the length of individual telomeres at single nucleotide resolution. Here the authors report a method to capture telomeres and accurately analyse their length in human cell lines and patient peripheral blood leukocyte samples using single-molecule real-time sequencing.

A recent paper by Jiang et al. in BMC Bioinformatics presented guidelines on long-read sequencing settings for structural variation (SV) calling, and benchmarked the performance of various SV calling tools, including NanoVar. In their simulation-based benchmarking, NanoVar was shown to perform poorly compared to other tools, mostly due to low SV recall rates. To investigate the causes for NanoVar's poor performance, we regenerated the simulation datasets (3× to 20×) as specified by Jiang et al. and performed benchmarking for NanoVar and Sniffles. Our results did not reflect the findings described by Jiang et al. In our analysis, NanoVar displayed more than three times the F1 scores and recall rates as reported in Jiang et al. across all sequencing coverages, indicating a previous underestimation of its performance. We also observed that NanoVar outperformed Sniffles in calling SVs with genotype concordance by more than 0.13 in F1 scores, which is contrary to the trend reported by Jiang et al. Besides, we identified multiple detrimental errors encountered during the analysis which were not addressed by Jiang et al. We hope that this commentary clarifies NanoVar's validity as a long-read SV caller and provides assurance to its users and the scientific community.

Self-renewal and differentiation are tightly controlled to maintain haematopoietic stem cell (HSC) homeostasis in the adult bone marrow 1 , 2 . During fetal development, expansion of HSCs (self-renewal) and production of differentiated haematopoietic cells (differentiation) are both required to sustain the haematopoietic system for body growth 3 , 4 . However, it remains unclear how these two seemingly opposing tasks are accomplished within the short embryonic period. Here we used in vivo genetic tracing in mice to analyse the formation of HSCs and progenitors from intra-arterial haematopoietic clusters, which contain HSC precursors and express the transcription factor hepatic leukaemia factor (HLF). Through kinetic study, we observed the simultaneous formation of HSCs and defined progenitors—previously regarded as descendants of HSCs 5 —from the HLF + precursor population, followed by prompt formation of the hierarchical haematopoietic population structure in the fetal liver in an HSC-independent manner. The transcription factor EVI1 is heterogeneously expressed within the precursor population, with EVI1 hi cells being predominantly localized to intra-embryonic arteries and preferentially giving rise to HSCs. By genetically manipulating EVI1 expression, we were able to alter HSC and progenitor output from precursors in vivo. Using fate tracking, we also demonstrated that fetal HSCs are slowly used to produce short-term HSCs at late gestation. These data suggest that fetal HSCs minimally contribute to the generation of progenitors and functional blood cells before birth. Stem cell-independent pathways during development thus offer a rational strategy for the rapid and simultaneous growth of tissues and stem cell pools. In fetal liver, the structure of the differentiated haemapoietic progenitor cell population is established directly from precursor cells, independently of haemapoietic stem cells.

The recent advent of third-generation sequencing technologies brings promise for better characterization of genomic structural variants by virtue of having longer reads. However, long-read applications are still constrained by their high sequencing error rates and low sequencing throughput. Here, we present NanoVar, an optimized structural variant caller utilizing low-depth (8X) whole-genome sequencing data generated by Oxford Nanopore Technologies. NanoVar exhibits higher structural variant calling accuracy when benchmarked against current tools using low-depth simulated datasets. In patient samples, we successfully validate structural variants characterized by NanoVar and uncover normal alternative sequences or alleles which are present in healthy individuals.

A recent paper by Jiang et al. in BMC Bioinformatics presented guidelines on long-read sequencing settings for structural variation (SV) calling, and benchmarked the performance of various SV calling tools, including NanoVar. In their simulation-based benchmarking, NanoVar was shown to perform poorly compared to other tools, mostly due to low SV recall rates. To investigate the causes for NanoVar's poor performance, we regenerated the simulation datasets (3x to 20x) as specified by Jiang et al. and performed benchmarking for NanoVar and Sniffles. Our results did not reflect the findings described by Jiang et al. In our analysis, NanoVar displayed more than three times the F1 scores and recall rates as reported in Jiang et al. across all sequencing coverages, indicating a previous underestimation of its performance. We also observed that NanoVar outperformed Sniffles in calling SVs with genotype concordance by more than 0.13 in F1 scores, which is contrary to the trend reported by Jiang et al. Besides, we identified multiple detrimental errors encountered during the analysis which were not addressed by Jiang et al. We hope that this commentary clarifies NanoVar's validity as a long-read SV caller and provides assurance to its users and the scientific community.

Third-generation long-read sequencing has enhanced the characterization of genomic structural variations (SV) in humans. However, due to its high cost and low throughput, its utilization in large-scale genetic studies or routine clinical investigations remains challenging. This thesis presents an innovative bioinformatics approach to overcome these limitations to allow cost-efficient and comprehensive discovery of SVs in the human genome. I develop NanoVar, an open-source bioinformatics software that enables the accurate discovery of SV at a lower sequencing depth and cost. At a sequencing depth of 8X, NanoVar outperformed existing tools in variant calling accuracy in simulated (F1 > 0.92) and real benchmark datasets. By employing NanoVar in a cohort of primary acute myeloid leukemia (AML) (n=17) and normal samples (n=7), my pilot study revealed 80 promising AML-associated variants. I hope that NanoVar may advance clinical SV research and expand our search for more druggable variants for precision medicine.

Acute Myeloid Leukemia (AML) is a highly lethal blood cancer arising due to aberrant differentiation of haematopoietic stem cells. MEIS1 and HOXA9 regulate stemness-related transcriptional programs in normal haematopoietic stem cells and AML. Here we obtained 3D genome organization maps in the CD34+ haematopoietic stem cells from 3 healthy individuals and 3 individuals with AML. The MEIS1 oncogenic transcription factor is regulated by a Frequently Interacting Region (FIRE). This FIRE is present in normal bone marrow samples, and an AML sample with high MEIS1 levels. However, it is absent in two AML samples that show low MEIS1 levels. CRISPR excision of the FIRE led to loss of MEIS1 and reduced cell growth. Moreover, MEIS1 can bind to the promoter of HOXA9, and HOXA9 can also auto-regulate by binding to its own promoter as well as an Acute Myeloid Leukemia-specific super-enhancer that interacts with the HOXA9 promoter via chromatin interactions. Competing Interest Statement M.J.F declares two patents on methodologies related to ChIA-PET. No other conflicts of interest are declared.

TIP60, a lysine acetyltransferase and H2AZ, a histone H2A variant are involved in transcription and DNA repair. Recent studies suggest that H2AZ acetylation is dependent on TIP60. Here, we show that TIP60 acetylates both isoforms of H2AZ in vitro and in cells. Utilizing ChIP-seq and RNA-seq to identify the genes regulated by TIP60-dependent acetylation of H2AZ, we find that TIP60-dependent acetylation of H2AZ correlates with the expression of genes involved in DNA damage repair, amongst several other pathways. In line with this, TIP60-depleted cells exhibit increased sensitivity to the DNA damage-inducing drug doxorubicin. Restoring the expression level of RAD51, one of the genes involved in the DNA damage repair pathway, partially rescues the doxorubicin sensitivity due to TIP60 depletion. Overall, our study uncovers a role for TIP60 in regulating doxorubicin-induced DNA damage sensitivity in a manner dependent on RAD51 transcription. Competing Interest Statement The authors have declared no competing interest.

Despite the increasing relevance of structural variants (SV) in the development of many human diseases, progress in novel pathological SV discovery remains impeded, partly due to the challenges of accurate and routine SV characterization in patients. The recent advent of third-generation sequencing (3GS) technologies brings promise for better characterization of genomic aberrations by virtue of having longer reads. However, the applications of 3GS are restricted by their high sequencing error rates and low sequencing throughput. To overcome these limitations, we present NanoVar, an accurate, rapid and low-depth (4X) 3GS SV caller utilizing long-reads generated by Oxford Nanopore Technologies. NanoVar employs split-reads and hard-clipped reads for SV detection and utilizes a neural network classifier for true SV enrichment. In simulated data, NanoVar demonstrated the highest SV detection accuracy (F1 score = 0.91) amongst other long-read SV callers using 12 gigabases (4X) of sequencing data. In patient samples, besides the detection of genomic aberrations, NanoVar also uncovered many normal alternative sequences or alleles which were present in healthy individuals. The low sequencing depth requirements of NanoVar enable the use of Nanopore sequencing for accurate SV characterization at a lower sequencing cost, an approach compatible with clinical studies and large-scale SV-association research.