A chromosome-scale genome sequence of sudangrass (Sorghum sudanense) highlights the genome evolution and regulation of dhurrin biosynthesis.

KEY MESSAGE: Sudangrass is more similar to US commercial sorghums than to cultivated sorghums from Africa sequence-wise and contain significantly lower dhurrin than sorghums. CYP79A1 is linked to dhurrin content in sorghum. Sudangrass [Sorghum sudanense (Piper) Stapf] is a hybrid between grain sorghum and its wild relative S. bicolor ssp. verticilliflorum and is grown as a forage crop due to its high biomass production and low dhurrin content compared to sorghum. In this study, we sequenced the sudangrass genome and showed that the assembled genome was 715.95 Mb with 35,243 protein-coding genes. Phylogenetic analysis with whole genome proteomes demonstrated that the sudangrass genome was more similar to US commercial sorghums than to its wild relatives and cultivated sorghums from Africa. We confirmed that at seedling stage, sudangrass accessions contained significantly lower dhurrin as measured by hydrocyanic acid potential (HCN-p) than cultivated sorghum accessions. Genome-wide association study identified a QTL most tightly associated with HCN-p and the linked SNPs were located in the 3' UTR of Sobic.001G012300 which encodes CYP79A1, the enzyme that catalyzes the first step of dhurrin biosynthesis. As in other grasses such as maize and rice, we also found that copia/gypsy long terminal repeat (LTR) retrotransposons were more abundant in cultivated than in wild sorghums, implying that crop domestication in the grasses was accompanied by increased copia/gypsy LTR retrotransposon insertions in the genomes.

CSMD: a computational subtraction-based microbiome discovery pipeline for species-level characterization of clinical metagenomic samples.

MOTIVATION: Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. RESULTS: We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. AVAILABILITY AND IMPLEMENTATION: CSMD is freely available at https://github.com/liuyu8721/csmd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Panax Quinquefolium Saponins Attenuate Myocardial Dysfunction Induced by Chronic Ischemia.

BACKGROUND/AIMS: Previous studies in rat models of myocardial ischemia showed that Panax quinquefolium saponins (PQS) could attenuate ischemic/reperfusion injury, increase vessel density and improve cardiac function. In the current study, we examined whether PQS could attenuate myocardial dysfunction in a swine model of chronic myocardial ischemia (CMI). METHODS: CMI was established in Bama mini-pigs by placing amroid constrictor on the left anterior descending artery (LAD). Starting from 2 months after the surgery, pigs randomly received PQS (30 mg/kg/day), atorvastatin (1.5 mg/kg/day), or no drug for one month (n=6). A group of pigs receiving sham surgery was included as an additional control. Glucose utilization was assessed with positron emission tomography-computer tomography (PET-CT). Cardiac function was assessed with echocardiography. Myocyte size, nuclear density, and arteriolar density were examined in tissue section obtained from the ischemia area. Potential molecular targets of PQS were identified using proteomic analysis with isobaric tags for relative and absolute quantitation (iTARQ) and network pharmacology. RESULTS: In comparison to the sham controls, pigs implanted with ameroid constrictor had decreased ventricular wall motion, left ventricular ejection fraction (LVEF), and glucose utilization. PQS significantly increased cardiac function and glucose utilization. Arteriole density and myocyte nuclear density were increased. Myocyte diameter was decreased. PQS also attenuated the CMI-induced change of protein expression profile. The effects of atorvastatin were generally similar to that of PQS. However, PQS attenuated the reduction of left ventricular systolic WT induced by CMI more robustly than atorvastatin. CONCLUSION: The results from the current study supports the use of PQS in patients with coronary artery disease.

Calmodulin 4 is dispensable for epidermal barrier formation and wound healing in mice.

Calcium-mediated signals play important roles in epidermal barrier formation, skin homoeostasis and wound repair. Calmodulin 4 (Calm4) is a small, Ca2+ -binding protein with strong expression in suprabasal keratinocytes. In mice, Calm4 first appears in the skin at the time of barrier formation, and its expression increases in response to epidermal barrier challenges. In this study, we report the generation of Calm4 knockout mice and provide evidence that Calm4 is dispensable for epidermal barrier formation, maintenance and repair.

Efficacy of photodynamic therapy as an adjunct to scaling and root planing on clinical parameters and microbial composition in subgingival plaque of periodontitis patients: A split-mouth randomized clinical trial.

BACKGROUND: The aim of this study was to assess the efficacy of photodynamic therapy (PDT) as an adjunct to scaling and root planing (SRP) on clinical parameters and microbial composition in subgingival plaque of periodontitis patients. METHODS: Seventeen patients were included in this split-mouth randomized clinical trial. Sites with probing pocket depth (PPD) ≥5 mm in combination with bleeding on probing in different quadrants were randomized into the control group, the group with a single PDT application right after SRP, and the group with three repeated PDT applications 1 week after SRP. The subgingival plaque was collected for 16S rRNA gene sequencing at baseline, Week 2, and Week 8. RESULTS: Seventeen patients with 60 sites completed this 8-week follow-up, and 157 subgingival plaques were successfully analyzed by sequencing. Significant improvements were observed in two primary outcomes: PPD at Week 8 and subgingival microbial composition. Compared to the control group, the repeated-PDT group showed a notable improvement in PPD, substantial alterations in the microbial profile, including a reduction in α-diversity and anaerobic bacteria, and an increase in aerobic bacteria at Week 2. Secondary outcomes, such as clinical attachment level and sulcus bleeding index, also showed improvement at Week 8. Furthermore, both the single- and repeated-PDT groups exhibited a decrease in periodontopathogens and an increase in beneficial bacteria compared with baseline. CONCLUSION: PDT promotes changes in the microbial composition of periodontitis patients' subgingival plaque in a direction favorable to periodontal health, and repeated PDT is a promising adjunctive therapy for periodontal treatment.

Evolving molecular HIV clusters revealed genotype-specific dynamics in Guangzhou, China (2008-2020).

OBJECTIVES: This study investigated the genotype-specific dynamics of molecular HIV clusters (MHCs) in Guangzhou, China, aiming to enhance HIV control. METHODS: HIV pol sequences from people with HIV (PWH) in Guangzhou (2008-2020) were obtained for genotyping and molecular network creation. MHCs were identified and categorized into three types: emerging, growing, or stable. Clustering rates, proportions of cluster types, and members within each type were calculated and their trends were assessed using joinpoint regression. RESULTS: Among 8395 PWH, the most prevalent HIV-1 genotypes were CRF07_BC (39.7%) and CRF01_AE (32.6%). The genotype composition has been stable since 2012 (Ps > 0.05). The overall clustering rate was 43.3%, with significant variations across genotypes (P < 0.001), indicating genotype-specific transmission fitness. Significant declines in overall and genotype-specific clustering rates toward the end of 2020 (Ps < 0.05), potentially offer support for HIV control efforts in reducing local infections. The continuously increasing proportions of stable clusters and the gradually decreasing proportions of emerging and growing clusters (either Ps < 0.05 or Ps > 0.05) suggest a trend toward stable molecular network structure. However, growing clusters exhibited CRF55_01B, CRF07_BC, and CRF59_01B dominance that indicate their priority for interventions. CONCLUSION: The evolving MHCs highlight the genotype-specific cluster dynamics, providing fresh insights for enhanced prevention and control strategies.

Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance.

Background: Precise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance. Methods: To avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries. Results: A total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring. Conclusion: An efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.

A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations.

Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19.

DeepMicrobes: taxonomic classification for metagenomics with deep learning.

Large-scale metagenomic assemblies have uncovered thousands of new species greatly expanding the known diversity of microbiomes in specific habitats. To investigate the roles of these uncultured species in human health or the environment, researchers need to incorporate their genome assemblies into a reference database for taxonomic classification. However, this procedure is hindered by the lack of a well-curated taxonomic tree for newly discovered species, which is required by current metagenomics tools. Here we report DeepMicrobes, a deep learning-based computational framework for taxonomic classification that allows researchers to bypass this limitation. We show the advantage of DeepMicrobes over state-of-the-art tools in species and genus identification and comparable accuracy in abundance estimation. We trained DeepMicrobes on genomes reconstructed from gut microbiomes and discovered potential novel signatures in inflammatory bowel diseases. DeepMicrobes facilitates effective investigations into the uncharacterized roles of metagenomic species.

Metagenomic analysis reveals gestational diabetes mellitus-related microbial regulators of glucose tolerance.

AIMS: Recent studies have suggested a possible association between microbiota and gestational diabetes (GDM). However, the results are inconsistent. Our objective was to investigate further the relationship between GDM and microbiota and verify the potential microbial marker. METHODS: Two complementary approaches were used for the demonstration. First, we compared the gut microbial composition of 23 GDM patients and 26 non-GDM ethnically Chinese Han pregnant women, by using whole-metagenome shotgun sequencing of their stool samples collected at the third trimester. Second, we used Q-PCR (quantitative polymerase chain reaction) to evaluate the gut microbial composition in the stool samples from another cohort of 150 Chinese pregnant women (113 Control and 37 GDM), to further confirm the potential microbial marker. RESULTS: The gut microbiota of GDM women show lower albeit not statistically significant (p = 0.18) alpha diversity at the species level than non-GDM women. However, the species-level beta-diversity or between-sample diversity measured by Bray-Curtis distance shows significant differences (p < 2.2e-16) between the two groups. The species Bacteroides dorei positively correlated with both OGTT (oral glucose tolerance test) 0-Hour (p = 0.0099) and OGTT 1-Hour (p = 0.0070). There is a similar trend between Bacteroides sp. 3_1_33FAA and both OGTT 0-Hour (p = 0.014) and OGTT 1-Hour (p = 0.0101) response variables. The species Alistipes putredinis negatively correlated with OGTT 1-Hour (p = 0.0172) and OGTT 2-Hour (p = 0.0147). Q-PCR validation further confirmed the association between the glucose tolerance loci of Bacteroides dorei and OGTT response. CONCLUSIONS: Gut microbiome is related to the diabetic status of Chinese women during pregnancy. Specific species such as Bacteroides dorei associate with glucose response and could be potential monitoring and therapeutic microbial markers for GDM.

Geographic Difference Shaped Human Ocular Surface Metagenome of Young Han Chinese From Beijing, Wenzhou, and Guangzhou Cities.

Purpose: Microbial ecosystems interact with the human body and affect human health. The microbial community on the ocular surface remains an underexplored territory despite its importance as the first line of defense barrier that protects the eye and ultimately sight. We investigated how age and sex affected human ocular surface microbiome, and in the present study wanted to understand how geographic difference shaped the microbiome in the ocular surface. Methods: We collected conjunctival specimens of 172 eyes from 86 healthy volunteers living in three Chinese cities, namely, Guangzhou, Wenzhou, and Beijing. Using the direct metagenomic shotgun sequencing approach, we characterized how geographic difference affected the human ocular microbiome. Results: We surveyed the taxonomic composition and metabolic function of the microbiota on human ocular surface. We showed that the ocular surface microbiota was composed of bacteria, viruses, and fungi. A geographical difference in both composition and function of the conjunctival microbiome suggests that the environment people lived in shapes their conjunctival microbiome, especially the dominate species. Conclusions: Our study provides a reference catalog of the healthy conjunctival metagenome and raises a concern for environmental influences on the ocular surface microbiome.

The DNA Methylation Inhibitor Zebularine Controls CD4+ T Cell Mediated Intraocular Inflammation.

CD4+ T cell mediated uveitis is conventionally treated with systemic immunosuppressive agents, including corticosteroids and biologics targeting key inflammatory cytokines. However, their long-term utility is limited due to various side effects. Here, we investigated whether DNA methylation inhibitor zebularine can target CD4+ T cells and control intraocular inflammation. Our results showed that zebularine restrained the expression of inflammatory cytokines IFN-γ and IL-17 in both human and murine CD4+ T cells in vitro. Importantly, it also significantly alleviated intraocular inflammation and retinal tissue damage in the murine experimental autoimmune uveitis (EAU) model in vivo, suggesting that the DNA methylation inhibitor zebularine is a candidate new therapeutic agent for uveitis.

Epigenetics, microbiota, and intraocular inflammation: New paradigms of immune regulation in the eye.

Sight threatening immune responses that damage the eye characterize intraocular inflammatory diseases. These diseases including uveitis and age-related macular degeneration are worryingly common and quality of life shattering. Genetic studies in past decades significantly advanced our understanding of the etiology of these devastating diseases. Unfortunately, patient genetics alone failed to adequately explain disease origin, susceptibility, and progression. Non-genetic factors such as the epigenetic regulation of ocular diseases and the environmental factors triggering intraocular inflammation offer new insight into intraocular inflammatory disorders. Importantly, mounting evidence is signaling that dysbiosis of human microbiota leads to rapid epigenomic reprograming of host cells and results in the onset of many diseases. In this review, we discuss how epigenetic mechanisms and microbiota may cooperate to initiate and perpetuate ocular inflammation. Lastly, we propose that the discovery of intraocular microbiota presents a significant shift in thought affecting current approaches to the diagnosis, treatment, and prevention of intraocular inflammatory diseases such as uveitis and age-related macular degeneration. The geographical and genetic background difference in both disease presentation and genetic association of intraocular inflammatory diseases may be due to the variation of intraocular microbiota.

DLX3-Dependent Regulation of Ion Transporters and Carbonic Anhydrases is Crucial for Enamel Mineralization.

Patients with tricho-dento-osseous (TDO) syndrome, an ectodermal dysplasia caused by mutations in the homeodomain transcription factor DLX3, exhibit enamel hypoplasia and hypomineralization. Here we used a conditional knockout mouse model to investigate the developmental and molecular consequences of Dlx3 deletion in the dental epithelium in vivo. Dlx3 deletion in the dental epithelium resulted in the formation of chalky hypomineralized enamel in all teeth. Interestingly, transcriptomic analysis revealed that major enamel matrix proteins and proteases known to be involved in enamel secretion and maturation were not affected significantly by Dlx3 deletion in the enamel organ. In contrast, expression of several ion transporters and carbonic anhydrases known to play an important role in enamel pH regulation during maturation was significantly affected in enamel organs lacking DLX3. Most of these affected genes showed binding of DLX3 to their proximal promoter as evidenced by chromatin immunoprecipitation sequencing (ChIP-seq) analysis on rat enamel organ. These molecular findings were consistent with altered pH staining evidenced by disruption of characteristic pH oscillations in the enamel. Taken together, these results show that DLX3 is indispensable for the regulation of ion transporters and carbonic anhydrases during the maturation stage of amelogenesis, exerting a crucial regulatory function on pH oscillations during enamel mineralization. © 2016 American Society for Bone and Mineral Research.

Panax Notoginseng Saponin Controls IL-17 Expression in Helper T Cells.

PURPOSE: Panax Notoginseng, a traditional Chinese medicine, is known as an anti-inflammatory herb. However, the molecular mechanism by which it controls helper T cell mediated immune responses is largely unknown. METHODS: Naive CD4+ T cells isolated from healthy donors, patients with Behcet's disease, and C57BL/6 mice were polarized into Th1, Th17, and Treg cells. Proliferation and cytokine expression were measured in these cells with the presence or absence of Panax Notoginseng saponins (PNS). Genomewide expression profiles of Th1, Th17, and Treg cells were assessed using Affymetrix microarray analysis. RESULTS: We found that PNS control the proliferation and differentiation of Th17 cells by globally downregulating the expression of inflammatory cytokines and cell cycle genes. CONCLUSIONS: These findings demonstrated that PNS function as an anti-inflammatory agent through directly targeting Th17 cell mediated immune response.

The effects of shared information on semantic calculations in the gene ontology.

The structured vocabulary that describes gene function, the gene ontology (GO), serves as a powerful tool in biological research. One application of GO in computational biology calculates semantic similarity between two concepts to make inferences about the functional similarity of genes. A class of term similarity algorithms explicitly calculates the shared information (SI) between concepts then substitutes this calculation into traditional term similarity measures such as Resnik, Lin, and Jiang-Conrath. Alternative SI approaches, when combined with ontology choice and term similarity type, lead to many gene-to-gene similarity measures. No thorough investigation has been made into the behavior, complexity, and performance of semantic methods derived from distinct SI approaches. We apply bootstrapping to compare the generalized performance of 57 gene-to-gene semantic measures across six benchmarks. Considering the number of measures, we additionally evaluate whether these methods can be leveraged through ensemble machine learning to improve prediction performance. Results showed that the choice of ontology type most strongly influenced performance across all evaluations. Combining measures into an ensemble classifier reduces cross-validation error beyond any individual measure for protein interaction prediction. This improvement resulted from information gained through the combination of ontology types as ensemble methods within each GO type offered no improvement. These results demonstrate that multiple SI measures can be leveraged for machine learning tasks such as automated gene function prediction by incorporating methods from across the ontologies. To facilitate future research in this area, we developed the GO Graph Tool Kit (GGTK), an open source C++ library with Python interface (github.com/paulbible/ggtk).

A novel DLX3-PKC integrated signaling network drives keratinocyte differentiation.

Epidermal homeostasis relies on a well-defined transcriptional control of keratinocyte proliferation and differentiation, which is critical to prevent skin diseases such as atopic dermatitis, psoriasis or cancer. We have recently shown that the homeobox transcription factor DLX3 and the tumor suppressor p53 co-regulate cell cycle-related signaling and that this mechanism is functionally involved in cutaneous squamous cell carcinoma development. Here we show that DLX3 expression and its downstream signaling depend on protein kinase C α (PKCα) activity in skin. We found that following 12-O-tetradecanoyl-phorbol-13-acetate (TPA) topical treatment, DLX3 expression is significantly upregulated in the epidermis and keratinocytes from mice overexpressing PKCα by transgenic targeting (K5-PKCα), resulting in cell cycle block and terminal differentiation. Epidermis lacking DLX3 (DLX3cKO), which is linked to the development of a DLX3-dependent epidermal hyperplasia with hyperkeratosis and dermal leukocyte recruitment, displays enhanced PKCα activation, suggesting a feedback regulation of DLX3 and PKCα. Of particular significance, transcriptional activation of epidermal barrier, antimicrobial peptide and cytokine genes is significantly increased in DLX3cKO skin and further increased by TPA-dependent PKC activation. Furthermore, when inhibiting PKC activity, we show that epidermal thickness, keratinocyte proliferation and inflammatory cell infiltration are reduced and the PKC-DLX3-dependent gene expression signature is normalized. Independently of PKC, DLX3 expression specifically modulates regulatory networks such as Wnt signaling, phosphatase activity and cell adhesion. Chromatin immunoprecipitation sequencing analysis of primary suprabasal keratinocytes showed binding of DLX3 to the proximal promoter regions of genes associated with cell cycle regulation, and of structural proteins and transcription factors involved in epidermal differentiation. These results indicate that Dlx3 potentially regulates a set of crucial genes necessary during the epidermal differentiation process. Altogether, we demonstrate the existence of a robust DLX3-PKCα signaling pathway in keratinocytes that is crucial to epidermal differentiation control and cutaneous homeostasis.

The Influence of Age and Sex on Ocular Surface Microbiota in Healthy Adults.

Purpose: A growing body of evidence suggests that the microbiome of the ocular surface confers potent immunoregulatory functions and has a key role in the physiologic maintenance of healthy eyes and in the pathogenesis of ocular diseases. Although the microbiome is known to be affected by age and sex, the influence of these factors on ocular surface microbiota in healthy adults remains largely unknown. Methods: Ocular surface microbiome samples were obtained from the inferior bulbar conjunctiva of 48 young and 42 old adults at Zhongshan Ophthalmic Center. Using metagenomic shotgun sequencing, we characterized the sex- and age-differences in conjunctival microbiome profiles of healthy adults. Results: Male and female groups differed only in the ß diversity of bacterial communities, while there were significant differences in bacterial composition, metabolic functions, and the abundance of antibiotic resistance genes between young and old adult groups. Conclusions: Our findings suggest that age and sex collectively shape the conjunctival microbiome, and may change the immune homeostasis of the ocular surface through alterations of its commensal microbiome.

PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond.

Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST's functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work.

A new algorithm for quantifying binding site pattern similarity with applications for Next Generation Sequencing.

New sources of regulatory data, such as transcription factor ChIP-seq experiments, can yield important insights into biological function through downstream analysis of motifs. Position Frequency Matrices (PFMs) are a standard format for representing transcription factor binding patterns. Comparison measures between these binding patterns are necessary to allow more sophisticated detection and classification of regulatory sequences. In this work we have developed a novel algorithm for gapped alignment of PFMs called PfmSim. We compare our measure with a standard measure, Sandelin and Wasserman, on similarity and classification tasks. Our measure gives better similarity values as evaluated by multiple tests.