Improved Prediction of Regulatory Element Using Hybrid Abelian Complexity Features with DNA Sequences

AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data

BACKGROUND

Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model's ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances-namely, local, medium, or distant associations.

FINDINGS

This article addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference and alignment free, providing additional methodologies for testing, including a highly flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar.

CONCLUSIONS

The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multithreading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at https://github.com/cobilab/alcor.

Super-enhancers: a new frontier for epigenetic modifiers in cancer chemoresistance

Although new developments of surgery, chemotherapy, radiotherapy, and immunotherapy treatments for cancer have improved patient survival, the emergence of chemoresistance in cancer has significant impacts on treatment effects. The development of chemoresistance involves several polygenic, progressive mechanisms at the molecular and cellular levels, as well as both genetic and epigenetic heterogeneities. Chemotherapeutics induce epigenetic reprogramming in cancer cells, converting a transient transcriptional state into a stably resistant one. Super-enhancers (SEs) are central to the maintenance of identity of cancer cells and promote SE-driven-oncogenic transcriptions to which cancer cells become highly addicted. This dependence on SE-driven transcription to maintain chemoresistance offers an Achilles' heel for chemoresistance. Indeed, the inhibition of SE components dampens oncogenic transcription and inhibits tumor growth to ultimately achieve combined sensitization and reverse the effects of drug resistance. No reviews have been published on SE-related mechanisms in the cancer chemoresistance. In this review, we investigated the structure, function, and regulation of chemoresistance-related SEs and their contributions to the chemotherapy via regulation of the formation of cancer stem cells, cellular plasticity, the microenvironment, genes associated with chemoresistance, noncoding RNAs, and tumor immunity. The discovery of these mechanisms may aid in the development of new drugs to improve the sensitivity and specificity of cancer cells to chemotherapy drugs.

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.