Genomics, proteomics & bioinformatics最新文献

Hematopoietic homeostasis is maintained by hematopoietic stem cells (HSCs), and it is tightly controlled at multiple levels to sustain the self-renewal capacity and differentiation potential of HSCs. Dysregulation of self-renewal and differentiation of HSCs leads to the development of hematologic diseases, including acute myeloid leukemia (AML). Thus, understanding the underlying mechanisms of HSC maintenance and the development of hematologic malignancies is one of the fundamental scientific endeavors in stem cell biology. N  6-methyladenosine (m6A) is a common modification in mammalian messenger RNAs (mRNAs) and plays important roles in various biological processes. In this study, we performed a comparative analysis of the dynamics of the RNA m6A methylome of hematopoietic stem and progenitor cells (HSPCs) and leukemia-initiating cells (LICs) in AML. We found that RNA m6A modification regulates the transformation of long-term HSCs into short-term HSCs and determines the lineage commitment of HSCs. Interestingly, m6A modification leads to reprogramming that promotes cellular transformation during AML development, and LIC-specific m6A targets are recognized by different m6A readers. Moreover, the very long chain fatty acid transporter ATP-binding cassette subfamily D member 2 (ABCD2) is a key factor that promotes AML development, and deletion of ABCD2 damages clonogenic ability, inhibits proliferation, and promotes apoptosis of human leukemia cells. This study provides a comprehensive understanding of the role of m6A in regulating cell state transition in normal hematopoiesis and leukemogenesis, and identifies ABCD2 as a key factor in AML development.

Long-range sequencing grants insight into additional genetic information beyond that which can be accessed by both short reads and modern long-read technology. Several new sequencing technologies are available for long-range datasets such as "Hi-C" and "Linked Reads" with high-throughput and high-resolution genome analysis, and are rapidly advancing the field of genome assembly, genome scaffolding, and more comprehensive variant identification. In this article, we focused on five major long-range sequencing technologies: high-throughput chromosome conformation capture (Hi-C), 10x Genomics Linked Reads, haplotagging, transposase enzyme linked long-read sequencing (TELL-seq), and single tube long fragment read (stLFR). We detailed the mechanisms and data products of the five platforms, introduced several of the most important applications, evaluated the quality of sequencing data from different platforms, and discussed the currently available bioinformatics tools. We hope this work will benefit the selection of appropriate long-range technology for specific biological studies.

The commonly-used drug susceptibility testing (DST) relies on bacterial culture and faces shortcomings such as long turnaround time and clone/subclone selection. We developed a targeted deep amplification sequencing (DAS) method directly applied to clinical specimens. In this DAS panel, we examined 941 drug-resistant mutations associated with 20 anti-tuberculosis drugs with an initial amount of 4 pg DNA and reduced clinical testing time from 20 days to two days. A prospective study was conducted using 115 clinical specimens mainly with Xpert® Mycobacterium tuberculosis/rifampicin (Xpert MTB/RIF) assay positive to evaluate drug-resistant mutation detection. DAS was performed on culture-free specimens, while culture-dependent isolates were used for phenotypic DST, DAS, and whole-genome sequencing (WGS). For in silico molecular DST, our result based on DAS panel revealed the similar accuracy to three published reports based on WGS. For 82 isolates, application of DAS showed better sensitivity (93.03% vs. 92.16%), specificity (96.10% vs. 95.02%), and accuracy (91.33% vs. 90.62%) than Mykrobe software using WGS. Compared to culture-dependent WGS, culture-free DAS provides a full picture of sequence variation at population level, exhibiting in detail the gain-and-loss variants caused by bacterial culture. Our study performs a systematic verification of the advantages of DAS in clinical applications and comprehensively illustrates the discrepancy in Mycobacterium tuberculosis before and after culture.

Cancer is a leading cause of death worldwide, and the identification of biomarkers and subtypes that can predict the long-term survival of cancer patients is essential for their risk stratification, treatment, and prognosis. However, there are currently no standardized tools for exploring cancer biomarkers or subtypes. In this study, we introduced Cancer Biomarker and Subtype Profiler (CBioProfiler), a web server and standalone application that includes two pipelines for analyzing cancer biomarkers and subtypes. The cancer biomarker pipeline consists of five modules for identifying and annotating cancer survival-related biomarkers using multiple survival-related machine learning algorithms. The cancer subtype pipeline includes three modules for data preprocessing, subtype identification using multiple unsupervised machine learning methods, as well as subtype evaluation and validation. CBioProfiler also includes CuratedCancerPrognosisData, a novel R package that integrates reviewed and curated gene expression and clinical data from 268 studies. These studies cover 43 common blood and solid tumors and draw upon 47,686 clinical samples. The web server is available at https://www.cbioprofiler.com/ and https://cbioprofiler.znhospital.cn/CBioProfiler/, and the standalone app and source code can be found at https://github.com/liuxiaoping2020/CBioProfiler.

Ribonuclease P (RNase P) was first described in the 1970's as an endoribonuclease acting in the maturation of precursor transfer RNAs (tRNAs). More recent studies, however, have uncovered non-canonical roles for RNase P and its components. Here, we review the recent progress of its involvement in chromatin assembly, DNA damage response, and maintenance of genome stability with implications in tumorigenesis. The possibility of RNase P as a therapeutic target in cancer is also discussed.

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.