Current Genomics最新文献

Background: One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets.

Objective: To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures.

Methods: In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used.

Results: In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods.

Conclusion: An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.

Background: Circular RNAs (circRNAs) are transcribed by RNA polymerase II and are mostly generated by the back-splicing of exons in the protein-coding gene. Massive circRNAs are reported to be differentially expressed in different species, implicating their prospects as aging biomarkers or regulators in the aging progression.

Methods: The possible role of circRNAs in aging and longevity was reviewed by the query of circRNAs from literature reports related to tissue, organ or cellular senescence, and individual longevity.

Results: A number of circRNAs have been found to positively and negatively modulate aging and longevity through canonical aging pathways in the invertebrates Caenorhabditis elegans and Drosophila. Recent studies have also shown that circRNAs regulate age-related processes and pathologies such various mammalian tissues, as the brain, serum, heart, and muscle. Besides, three identified representative circRNAs (circSfl, circGRIA1, and circNF1-419) were elucidated to correlate with aging and longevity.

Conclusion: This review outlined the current studies of circRNAs in aging and longevity, highlighting the role of circRNAs as a biomarker of aging and as a regulator of longevity.

Preimplantation Genetic Testing (PGT) aims to reduce the chance of an affected pregnancy or improve success in an assisted reproduction cycle. Since the first established pregnancies in 1990, methodological approaches have greatly evolved, combined with significant advances in the embryological laboratory. The application of preimplantation testing has expanded, while the accuracy and reliability of monogenic and chromosomal analysis have improved. The procedure traditionally employs an invasive approach to assess the nucleic acid content of embryos. All biopsy procedures require high technical skill, and costly equipment, and may impact both the accuracy of genetic testing and embryo viability. To overcome these limitations, many researchers have focused on the analysis of cell-free DNA (cfDNA) at the preimplantation stage, sampled either from the blastocoel or embryo culture media, to determine the genetic status of the embryo non-invasively. Studies have assessed the origin of cfDNA and its application in non-invasive testing for monogenic disease and chromosomal aneuploidies. Herein, we discuss the state-of-the-art for modern non-invasive embryonic genetic material assessment in the context of PGT. The results are difficult to integrate due to numerous methodological differences between the studies, while further work is required to assess the suitability of cfDNA analysis for clinical application.

Genome sequences indicate a wide variety of characteristics, which include species and sub-species type, genotype, diseases, growth indicators, yield quality, etc. To analyze and study the characteristics of the genome sequences across different species, various deep learning models have been proposed by researchers, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Multilayer Perceptrons (MLPs), etc., which vary in terms of evaluation performance, area of application and species that are processed. Due to a wide differentiation between the algorithmic implementations, it becomes difficult for research programmers to select the best possible genome processing model for their application. In order to facilitate this selection, the paper reviews a wide variety of such models and compares their performance in terms of accuracy, area of application, computational complexity, processing delay, precision and recall. Thus, in the present review, various deep learning and machine learning models have been presented that possess different accuracies for different applications. For multiple genomic data, Repeated Incremental Pruning to Produce Error Reduction with Support Vector Machine (Ripper SVM) outputs 99.7% of accuracy, and for cancer genomic data, it exhibits 99.27% of accuracy using the CNN Bayesian method. Whereas for Covid genome analysis, Bidirectional Long Short-Term Memory with CNN (BiLSTM CNN) exhibits the highest accuracy of 99.95%. A similar analysis of precision and recall of different models has been reviewed. Finally, this paper concludes with some interesting observations related to the genomic processing models and recommends applications for their efficient use.

In the decade since non-invasive prenatal testing (NIPT) was first implemented as a prenatal screening tool, it has gained recognition for its sensitivity and specificity in the detection of common aneuploidies. This review mainly focuses on the emerging role of NIPT in pregnancies following assisted reproductive technology (ART) in the light of current evidence and recommendations. It also deals with the challenges, shortcomings and interpretational difficulties related to NIPT in ART pregnancies, with particular emphasis on twin and vanishing twin pregnancies, which are widely regarded as the Achilles' heel of most pre-natal screening platforms. Future directions for exploration towards improving the performance and extending the scope of NIPT are also addressed.

Objective: Ovarian cancer is a kind of common gynecological malignancy in women. PARP inhibitors (PARPi) have been approved for ovarian cancer treatment. However, the primary and acquired resistance have limited the application of PARPi. The mechanisms remain to be elucidated. Methods: In this study, we characterized the expression profiles of mRNA and nonconding RNAs (ncRNAs) and constructed the regulatory networks based on RNA sequencing in PARPi Olaparib-induced ovarian cancer cells. Results: We found that the functions of the differentially expressed genes were enriched in "PI3K/AKT signaling pathway," "MAPK signaling pathway" and "metabolic process". The functions of DELs (cis) were enriched in "Human papillomavirus infection""tight junction" "MAPK signaling pathway". As the central regulator of ceRNAs, the differentially expressed miRNAs were enriched in "Human papillomavirus infection" "MAPK signaling pathway" "Ras signaling pathway". According to the degree of interaction, we identified 3 lncRNAs, 2 circRNAs, 7 miRNAs, and 12 mRNA as the key regulatory ceRNA axis, in which miR-320b was the important mediator. Conclusion: Here, we revealed the key regulatory lncRNA (circRNA)-miRNA-mRNA axis and their involved pathways in the PARPi resistant ovarian cancer cells. These findings provide new insights into exploring the ceRNA regulatory networks and developing new targets for PARPi resistance.

Background: The currently available methods for sexing human or mouse cells have weaknesses. Therefore, it is necessary to establish new methods. Methods: We used bioinformatics approach to identify genes that have alleles on both the X and Y chromosomes of mouse and human genomes and have a region showing a significant difference between the X and Y alleles. We then used polymerase chain reactions (PCR) followed by visualization of the PCR amplicons in agarose gels to establish these genomic regions as genetic sex markers. Results: Our bioinformatics analyses identified eight mouse sex markers and 56 human sex markers that are new, i.e. are previously unreported. Six of the eight mouse markers and 14 of the 56 human markers were verified using PCR and ensuing visualization of the PCR amplicons in agarose gels. Most of the tested and untested sex markers possess significant differences in the molecular weight between the X- and Y-derived PCR amplicons and are thus much better than most, if not all, previously-reported genetic sex markers. We also established several simple and essentially cost-free methods for extraction of crude genomic DNA from cultured cells, blood samples, and tissues that could be used as template for PCR amplification. Conclusion: We have established new sex genetic markers and methods for extracting genomic DNA and for sexing human and mouse cells. Our work may also lend some methodological strategies to the identification of new genetic sex markers for other organismal species.

In the post-genomic era, data management and development of bioinformatic tools are critical for the adequate exploitation of genomics data. In this review, we address the actual situation for the subset of crops represented by the perennial fruit species. The agronomical singularity of these species compared to plant and crop model species provides significant challenges on the implementation of good practices generally not addressed in other species. Studies are usually performed over several years in non-controlled environments, usage of rootstock is common, and breeders heavily rely on vegetative propagation. A reference genome is now available for all the major species as well as many members of the economically important genera for breeding purposes. Development of pangenome for these species is beginning to gain momentum which will require a substantial effort in term of bioinformatic tool development. The available tools for genome annotation and functional analysis will also be presented.

Background: Plastids are plant-specific semi-autonomous self-replicating organelles, containing circular DNA molecules called plastomes. Plastids perform crucial functions, including photosynthesis, stress perception and response, synthesis of metabolites, and storage. The plastome and plastid numbers have been shown to be modulated by developmental stage and environmental stimuli and have been used as a biomarker (identification of plant species) and biosensor (an indicator of abiotic and biotic stresses). However, the determination of plastome sequence and plastid number is a laborious process requiring sophisticated equipment. Methods: This study proposes using plastome copy number (PCN), which can be determined rapidly by real-time quantitative polymerase chain reaction (RT-qPCR) as a plant product quality biomarker. This study shows that the PCN log₁₀ and range PCN log₁₀ values calculated from RT-qPCR data, which was obtained for two years from leaves and lint samples of cotton and seed samples of cotton, rice, soybean, maize, and sesame can be used for assessing the quality of the samples. Results: Observation of lower range PCN log₁₀ values for CS (0.31) and CR (0.58) indicated that the PCN showed little variance from the mean PCN log₁₀ values for CS (3.81) and CR (3.85), suggesting that these samples might have encountered ambient environmental conditions during growth and/ or post-harvest storage and processing. This conclusion was further supported by observation of higher range PCN log₁₀ values for RS (3.09) versus RP (0.05), where rice seeds in the RP group had protective hull covering compared to broken hull-less seeds in the RS group. To further support that PCN is affected by external factors, rice seeds treated with high temperatures and pathogens exhibited lower PCN values when compared to untreated seeds. Furthermore, the range PCN log₁₀ values were found to be high for cotton leaf (CL) and lint (Clt) sample groups, 4.11 and 3.63, respectively, where leaf and lint samples were of different sizes, indicating that leaf samples might be of different developmental stage and lint samples might have been processed differently, supporting that the PCN is affected by both internal and external factors, respectively. Moreover, PCN log₁₀ values were found to be plant specific, with oil containing seeds such as SeS (6.49) and MS (5.05) exhibiting high PCN log₁₀ values compared to non-oil seeds such as SS (1.96). Conclusion: In conclusion, it was observed that PCN log₁₀ values calculated from RT-qPCR assays were specific to plant species and the range of PCN log₁₀ values can be directly correlated to the internal and external factors and, therefore might be used as a potential biomarker for assessing the quality of plant products.