EgoNet: Identification of human disease ego-network modules

Background: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.Results: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.Conclusions: Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases

Monitoring snow-cover area change in Antarctic coastline region using MODIS product data

Based on MODIS snow products, this article studied the changes of snow cover area during 2003—2006 along the coastline of the Antarctic, and 18 typical regions were chosen for further analysis. The result showed that the change of snow cover area was in a fluctuant downward trend as a whole, and more fluctuated obviously in warm season than in cold season. In temporal scale: for the season cycle, the snow cover extent increased rapidly in cold season (Apr-Oct) , while the performance in warm season (Nov-Mar) was not exactly the same during the four years, the snow cover extent decreased in the first and then increased in 2004 and 2006, however, increased firstly and then decreased but reduced as a whole in 2005, for the inter-annual cycle, snow cover extent was the largest in 2003, but reached to the lowest level in 2004, and then increased gradually in 2005 and 2006, whereas, it declined with fluctuant as a whole. In spatial scale, changes mainly centralized along the coastline, moreover, it was more remarkable in the West Antarctic than in the East Antarctic, especially in the Antarctic Peninsula region

CAVEAT ON THE ERROR ANALYSIS FOR STEREOLOGICAL ESTIMATES

It is frequently asked that how big a sample size, or how much measurement, is needed to achieve an accuratestereological estimate. The observed total error of a stereological estimate arises from individual difference (i.e. i ter-animal / organ difference or biological variation) and intra-individual variation (or the stereological error). Statistical methods for error analysis familiar to most biological researchers are based on independent random sampling, however systematic random sampling, which is usually more efficient, is almost always performed in practice. A number of methods for error analysis were utilized in a number of model and actual studies in this paper to demonstrate from a practical point of view the pros and cons of different error analytical methods. Assumption of independence for a systematic sampling will result in overestimation of the stereological error as shown by the studies. A simple and practical approach for error analysis as recommended in this paper is to divide the systematic sample from an organ into two systematic sub-samples, regard them as two independent sub-samples and then compare the difference between the two sub-sample means

Transcriptome Phase Distribution Analysis Reveals Diurnal Regulated Biological Processes and Key Pathways in Rice Flag Leaves and Seedling Leaves

Plant diurnal oscillation is a 24-hour period based variation. The correlation between diurnal genes and biological pathways was widely revealed by microarray analysis in different species. Rice (Oryza sativa) is the major food staple for about half of the world's population. The rice flag leaf is essential in providing photosynthates to the grain filling. However, there is still no comprehensive view about the diurnal transcriptome for rice leaves. In this study, we applied rice microarray to monitor the rhythmically expressed genes in rice seedling and flag leaves. We developed a new computational analysis approach and identified 6,266 (10.96%) diurnal probe sets in seedling leaves, 13,773 (24.08%) diurnal probe sets in flag leaves. About 65% of overall transcription factors were identified as flag leaf preferred. In seedling leaves, the peak of phase distribution was from 2:00am to 4:00am, whereas in flag leaves, the peak was from 8:00pm to 2:00am. The diurnal phase distribution analysis of gene ontology (GO) and cis-element enrichment indicated that, some important processes were waken by the light, such as photosynthesis and abiotic stimulus, while some genes related to the nuclear and ribosome involved processes were active mostly during the switch time of light to dark. The starch and sucrose metabolism pathway genes also showed diurnal phase. We conducted comparison analysis between Arabidopsis and rice leaf transcriptome throughout the diurnal cycle. In summary, our analysis approach is feasible for relatively unbiased identification of diurnal transcripts, efficiently detecting some special periodic patterns with non-sinusoidal periodic patterns. Compared to the rice flag leaves, the gene transcription levels of seedling leaves were relatively limited to the diurnal rhythm. Our comprehensive microarray analysis of seedling and flag leaves of rice provided an overview of the rice diurnal transcriptome and indicated some diurnal regulated biological processes and key functional pathways in rice

Construction of a one-step multiplex real-time PCR assay for the detection of serogroups A, B, and E of Pasteurella multocida associated with bovine pasteurellosis

Bovine pasteurellosis, caused by serogroups A, B, and E of Pasteurella multocida (Pm), is mainly manifested as bovine respiratory disease (BRD) and hemorrhagic septicemia (HS). The disease has caused a great economic loss for the cattle industry globally. Therefore, identifying the Pm serogroups is critical for optimal diagnosis and subsequent clinical treatment and even epidemiological studies. In this study, a one-step multiplex real-time PCR assay was established. Three pairs of specific primers were prepared to detect the highly conserved genomic regions of serogroups A (HyaD), B (bcbD), and E (ecbJ) of Pm, respectively. The results depicted that the method had no cross-reaction with other bovine pathogens (Mannheimia hemolytica, Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, Salmonella Dublin, Mycobacterium paratuberculosis, infectious bovine rhinotracheitis virus, and Mycoplasma bovis). The linear range (107 to 102 copies/μL) showed the R2 values for serogroups A, B, and E of Pm as 0.9975, 0.9964, and 0.996, respectively. The multiplex real-time PCR efficiency was 90.30%, 90.72%, and 90.57% for CartA, CartB, and CartE, respectively. The sensitivity result showed that the serogroups A, B, and E of Pm could be detected to be as low as 10 copies/μL. The repeatability result clarified that an intra-assay and an inter-assay coefficient of variation of serogroups A, B, and E of Pm was < 2%. For the clinical samples, the detection rate was higher than the OIE-recommended ordinary PCR. Overall, the established one-step multiplex real-time PCR assay may be a valuable tool for the rapid and early detection of the serogroups A, B, and E of Pm with high specificity and sensitivity

Opposing transcriptional programs of KLF5 and AR emerge during therapy for advanced prostate cancer.

Endocrine therapies for prostate cancer inhibit the androgen receptor (AR) transcription factor. In most cases, AR activity resumes during therapy and drives progression to castration-resistant prostate cancer (CRPC). However, therapy can also promote lineage plasticity and select for AR-independent phenotypes that are uniformly lethal. Here, we demonstrate the stem cell transcription factor Krüppel-like factor 5 (KLF5) is low or absent in prostate cancers prior to endocrine therapy, but induced in a subset of CRPC, including CRPC displaying lineage plasticity. KLF5 and AR physically interact on chromatin and drive opposing transcriptional programs, with KLF5 promoting cellular migration, anchorage-independent growth, and basal epithelial cell phenotypes. We identify ERBB2 as a point of transcriptional convergence displaying activation by KLF5 and repression by AR. ERBB2 inhibitors preferentially block KLF5-driven oncogenic phenotypes. These findings implicate KLF5 as an oncogene that can be upregulated in CRPC to oppose AR activities and promote lineage plasticity

PxBLAT: an efficient python binding library for BLAT

Abstract Background With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem. Results PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT. Conclusions PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment