bluuebunny/crossref_metadata_2025_split huggingface dataset:
MGMR: leveraging RNA-Seq population data to optimize expression estimation
Authors: Rozov Roye , Halperin Eran , Shamir Ron 
Published: December 2019

Abstract

Background
RNA-Seq is a technique that uses Next Generation Sequencing to identify transcripts and estimate transcription levels. When applying this technique for quantification, one must contend with reads that align to multiple positions in the genome (multireads). Previous efforts to resolve multireads have shown that RNA-Seq expression estimation can be improved using probabilistic allocation of reads to genes. These methods use a probabilistic generative model for data generation and resolve ambiguity using likelihood-based approaches. In many instances, RNA-seq experiments are performed in the context of a population. The generative models of current methods do not take into account such population information, and it is an open question whether this information can improve quantification of the individual samples

Results
In order to explore the contribution of population level information in RNA-seq quantification, we apply a hierarchical probabilistic generative model, which assumes that expression levels of different individuals are sampled from a Dirichlet distribution with parameters specific to the population, and reads are sampled from the distribution of expression levels. We introduce an optimization procedure for the estimation of the model parameters, and use HapMap data and simulated data to demonstrate that the model yields a significant improvement in the accuracy of expression levels of paralogous genes.

Conclusions
We provide a proof of principal of the benefit of drawing on population commonalities to estimate expression. The results of our experiments demonstrate this approach can be beneficial, primarily for estimation at the gene level.
Bioinformatics in Italy: BITS2012, the ninth annual meeting of the Italian Society of Bioinformatics
Authors: Gissi Carmela , Romano Paolo , Ferro Alfredo , Giugno Rosalba , Pulvirenti Alfredo , Facchiano Angelo , Helmer-Citterich Manuela 
Published: December 2019

Abstract
The BITS2012 meeting, held in Catania on May 2-4, 2012, brought together almost 100 Italian researchers working in the field of Bioinformatics, as well as students in the same or related disciplines. About 90 original research works were presented either as oral communication or as posters, representing a landscape of Italian current research in bioinformatics.
This preface provides a brief overview of the meeting and introduces the manuscripts that were accepted for publication in this supplement, after a strict and careful peer-review by an International board of referees.
Integrating peptides' sequence and energy of contact residues information improves prediction of peptide and HLA-I binding with unknown alleles
Authors: Luo Fei , Gao Yangyang , Zhu Yongqiong , Liu Juan 
Published: December 2019

AbstractBackgroundThe HLA (human leukocyte antigen) class I is a kind of molecule encoded by a large family of genes and is characteristic of high polymorphism. Now the number of the registered HLA-I molecules has exceeded 3000. Slight differences in the amino acid sequences of HLAs would make them bind to different sets of peptides. In the past decades, although many methods have been proposed to predict the binding between peptides and HLA-I molecules and achieved good performance, most experimental data used by them is limited to the HLAs with a small number of alleles. Thus they are inclined to obtain high prediction accuracy only for data with similar alleles. Because the peptides and HLAs together determine the binding, it's necessary to consider their contribution meanwhile.ResultsBy taking into account the features of the peptides sequence and the energy of contact residues, in this paper a method based on the artificial neural network is proposed to predict the binding of peptides and HLA-I even when the HLAs' potential alleles are unknown. Two experiments in the allele-specific and super-type cases are performed respectively to validate our method. In the first case, we collect 14 HLA-A and 14 HLA-B molecules on Bjoern Peters dataset, and compare our method with the ARB, SMM, NetMHC and other 16 online methods. Our method gets the best average AUC (Area under the ROC) value as 0.909. In the second one, we use leave one out cross validation on MHC-peptide binding data that has different alleles but shares the common super-type. Compared to gold standard methods like NetMHC and NetMHCpan, our method again achieves the best average AUC value as 0.847.ConclusionsOur method achieves satisfactory results. Whenever it's tested on the HLA-I with single definite gene or with super-type gene locus, it gets better classification accuracy. Especially, when the training set is small, our method still works better than the other methods in the comparison. Therefore, we could make a conclusion that by combining the peptides' information, HLAs amino acid residues' interaction information and contact energy, our method really could improve prediction of the peptide HLA-I binding even when there aren't the prior experimental dataset for HLAs with various alleles.
Comprehensive analysis of microRNAs in breast cancer
Authors: Chang Hong-Tai , Li Sung-Chou , Ho Meng-Ru , Pan Hung-Wei , Ger Luo-Ping , Hu Ling-Yueh , Yu Shou-Yu , Li Wen-Hsiung , Tsai Kuo-Wang 
Published: December 2019

Abstract

Background
MicroRNAs (miRNAs) are short noncoding RNAs (approximately 22 nucleotides in length) that play important roles in breast cancer progression by downregulating gene expression. The detailed mechanisms and biological functions of miRNA molecules in breast carcinogenesis have yet to be fully elucidated. This study used bioinformatics and experimental approaches to conduct detailed analysis of the dysregulated miRNAs, arm selection preferences, 3' end modifications, and position shifts in isoforms of miRNAs (isomiRs) in breast cancer.

Methods
Next-generation sequencing (NGS) data on breast cancer was obtained from the NCBI Sequence Read Archive (SRA). The miRNA expression profiles and isomiRs in normal breast and breast tumor tissues were determined by mapping the clean reads back to human miRNAs. Differences in miRNA expression and pre-miRNA 5p/3p arm usage between normal and breast tumor tissues were further investigated using stem-loop reverse transcription and real-time polymerase chain reaction.

Results
The analysis identified and confirmed the aberrant expression of 22 miRNAs in breast cancer. Results from pathway enrichment analysis further indicated that the aberrantly expressed miRNAs play important roles in breast carcinogenesis by regulating the mitogen-activated protein kinase (MAPK) signaling pathway. Data also indicated that the position shifts in isomiRs and 3' end modifications were consistent in breast tumor and adjacent normal tissues, and that 5p/3p arm usage of some miRNAs displayed significant preferences in breast cancer.

Conclusions
Expression pattern and arm selection of miRNAs are significantly varied in breast cancers through analyzing NGS data and experimental approach. These miRNA candidates have high potential to play critical roles in the progression of breast cancer and could potentially provide as targets for future therapy.
Blocking Protein kinase C signaling pathway: mechanistic insights into the anti-leishmanial activity of prospective herbal drugs from Withania somnifera
Authors: Grover Abhinav , Katiyar Shashank Prakash , Jeyakanthan Jeyaraman , Dubey Vikash Kumar , Sundar Durai 
Published: December 2019

Abstract

Background
Leishmaniasis is caused by several species of leishmania protozoan and is one of the major vector-born diseases after malaria and sleeping sickness. Toxicity of available drugs and drug resistance development by protozoa in recent years has made Leishmaniasis cure difficult and challenging. This urges the need to discover new antileishmanial-drug targets and antileishmanial-drug development.

Results
Tertiary structure of leishmanial protein kinase C was predicted and found stable with a RMSD of 5.8Å during MD simulations. Natural compound withaferin A inhibited the predicted protein at its active site with -28.47 kcal/mol binding free energy. Withanone was also found to inhibit LPKC with good binding affinity of -22.57 kcal/mol. Both withaferin A and withanone were found stable within the binding pocket of predicted protein when MD simulations of ligand-bound protein complexes were carried out to examine the consistency of interactions between the two.

Conclusions
Leishmanial protein kinase C (LPKC) has been identified as a potential target to develop drugs against Leishmaniasis. We modelled and refined the tertiary structure of LPKC using computational methods such as homology modelling and molecular dynamics simulations. This structure of LPKC was used to reveal mode of inhibition of two previous experimentally reported natural compounds from Withania somnifera - withaferin A and withanone.
Literature aided determination of data quality and statistical significance threshold for gene expression studies
Authors: Xu Lijing , Cheng Cheng , George E Olusegun , Homayouni Ramin 
Published: December 2019

Abstract

Background
Gene expression data are noisy due to technical and biological variability. Consequently, analysis of gene expression data is complex. Different statistical methods produce distinct sets of genes. In addition, selection of expression p-value (EPv) threshold is somewhat arbitrary. In this study, we aimed to develop novel literature based approaches to integrate functional information in analysis of gene expression data.

Methods
Functional relationships between genes were derived by Latent Semantic Indexing (LSI) of Medline abstracts and used to calculate the function cohesion of gene sets. In this study, literature cohesion was applied in two ways. First, Literature-Based Functional Significance (LBFS) method was developed to calculate a p-value for the cohesion of differentially expressed genes (DEGs) in order to objectively evaluate the overall biological significance of the gene expression experiments. Second, Literature Aided Statistical Significance Threshold (LASST) was developed to determine the appropriate expression p-value threshold for a given experiment.

Results
We tested our methods on three different publicly available datasets. LBFS analysis demonstrated that only two experiments were significantly cohesive. For each experiment, we also compared the LBFS values of DEGs generated by four different statistical methods. We found that some statistical tests produced more functionally cohesive gene sets than others. However, no statistical test was consistently better for all experiments. This reemphasizes that a statistical test must be carefully selected for each expression study. Moreover, LASST analysis demonstrated that the expression p-value thresholds for some experiments were considerably lower (p < 0.02 and 0.01), suggesting that the arbitrary p-values and false discovery rate thresholds that are commonly used in expression studies may not be biologically sound.

Conclusions
We have developed robust and objective literature-based methods to evaluate the biological support for gene expression experiments and to determine the appropriate statistical significance threshold. These methods will assist investigators to more efficiently extract biologically meaningful insights from high throughput gene expression experiments.
A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing
Authors: Chou Chih-Hung , Lin Feng-Mao , Chou Min-Te , Hsu Sheng-Da , Chang Tzu-Hao , Weng Shun-Long , Shrestha Sirjana , Hsiao Chiung-Chih , Hung Jui-Hung , Huang Hsien-Da 
Published: December 2019

Abstract

Background
MicroRNAs (miRNAs) play a critical role in down-regulating gene expression. By coupling with Argonaute family proteins, miRNAs bind to target sites on mRNAs and employ translational repression. A large amount of miRNA-target interactions (MTIs) have been identified by the crosslinking and immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion in miRNA-RNA-protein crosslinking regions. This artificial error obviously reduces the mappability of reads. However, a specific tool to analyze CLIP and PAR-CLIP data that takes T to C conversion into account is still in need.

Results
We herein propose the first CLIP and PAR-CLIP sequencing analysis platform specifically for miRNA target analysis, namely miRTarCLIP. From scratch, it automatically removes adaptor sequences from raw reads, filters low quality reads, reverts C to T, aligns reads to 3'UTRs, scans for read clusters, identifies high confidence miRNA target sites, and provides annotations from external databases. With multi-threading techniques and our novel C to T reversion procedure, miRTarCLIP greatly reduces the running time comparing to conventional approaches. In addition, miRTarCLIP serves with a web-based interface to provide better user experiences in browsing and searching targets of interested miRNAs. To demonstrate the superior functionality of miRTarCLIP, we applied miRTarCLIP to two public available CLIP and PAR-CLIP sequencing datasets. miRTarCLIP not only shows comparable results to that of other existing tools in a much faster speed, but also reveals interesting features among these putative target sites. Specifically, we used miRTarCLIP to disclose that T to C conversion within position 1-7 and that within position 8-14 of miRNA target sites are significantly different (p value = 0.02), and even more significant when focusing on sites targeted by top 102 highly expressed miRNAs only (p value = 0.01). These results comply with previous findings and further suggest that combining miRNA expression and PAR-CLIP data can improve accuracy of the miRNA target prediction.

Conclusion
To sum up, we devised a systematic approach for mining miRNA-target sites from CLIP-seq and PAR-CLIP sequencing data, and integrated the workflow with a graphical web-based browser, which provides a user friendly interface and detailed annotations of MTIs. We also showed through real-life examples that miRTarCLIP is a powerful tool for understanding miRNAs. Our integrated tool can be accessed online freely at http://miRTarCLIP.mbc.nctu.edu.tw.
Characterization and genomic analysis of chromate resistant and reducing Bacillus cereus strain SJ1
Authors: He Minyan , Li Xiangyang , Guo Liang , Miller Susan J , Rensing Christopher , Wang Gejiao 
Published: December 2019

Abstract

Background
Chromium is a toxic heavy metal, which primarily exists in two inorganic forms, Cr(VI) and Cr(III). Chromate [Cr(VI)] is carcinogenic, mutational, and teratogenic due to its strong oxidizing nature. Biotransformation of Cr(VI) to less-toxic Cr(III) by chromate-resistant and reducing bacteria has offered an ecological and economical option for chromate detoxification and bioremediation. However, knowledge of the genetic determinants for chromate resistance and reduction has been limited so far. Our main aim was to investigate chromate resistance and reduction by Bacillus cereus SJ1, and to further study the underlying mechanisms at the molecular level using the obtained genome sequence.

Results

Bacillus cereus SJ1 isolated from chromium-contaminated wastewater of a metal electroplating factory displayed high Cr(VI) resistance with a minimal inhibitory concentration (MIC) of 30 mM when induced with Cr(VI). A complete bacterial reduction of 1 mM Cr(VI) was achieved within 57 h. By genome sequence analysis, a putative chromate transport operon, chrIA 1, and two additional chrA genes encoding putative chromate transporters that likely confer chromate resistance were identified. Furthermore, we also found an azoreductase gene azoR and four nitroreductase genes nitR possibly involved in chromate reduction. Using reverse transcription PCR (RT-PCR) technology, it was shown that expression of adjacent genes chrA 1 and chrI was induced in response to Cr(VI) but expression of the other two chromate transporter genes chrA 2 and chrA 3 was constitutive. In contrast, chromate reduction was constitutive in both phenotypic and gene expression analyses. The presence of a resolvase gene upstream of chrIA 1, an arsenic resistance operon and a gene encoding Tn7-like transposition proteins ABBCCCD downstream of chrIA 1 in B. cereus SJ1 implied the possibility of recent horizontal gene transfer.

Conclusion
Our results indicate that expression of the chromate transporter gene chrA 1 was inducible by Cr(VI) and most likely regulated by the putative transcriptional regulator ChrI. The bacterial Cr(VI)-resistant level was also inducible. The presence of an adjacent arsenic resistance gene cluster nearby the chrIA 1 suggested that strong selective pressure by chromium and arsenic could cause bacterial horizontal gene transfer. Such events may favor the survival and increase the resistance level of B. cereus SJ1.
Significance of Cuscutain, a cysteine protease from Cuscuta reflexa, in host-parasite interactions
Authors: Bleischwitz Marc , Albert Markus , Fuchsbauer Hans-Lothar , Kaldenhoff Ralf 
Published: December 2019

AbstractBackgroundPlant infestation with parasitic weeds likeCuscuta reflexainduces morphological as well as biochemical changes in the host and the parasite. These modifications could be caused by a change in protein or gene activity. Using a comparative macroarray approachCuscutagenes specifically upregulated at the host attachment site were identified.ResultsOne of the infestation specificCuscutagenes encodes a cysteine protease. The protein and its intrinsic inhibitory peptide were heterologously expressed, purified and biochemically characterized. The haustoria specific enzyme was named cuscutain in accordance with similar proteins from other plants, e.g. papaya. The role of cuscutain and its inhibitor during the host parasite interaction was studied by external application of an inhibitor suspension, which induced a significant reduction of successful infection events.ConclusionsThe study provides new information about molecular events during the parasitic plant - host interaction. Inhibition of cuscutain cysteine proteinase could provide means for antagonizing parasitic plants.
Molecular basis for defect in Alix-binding by alternatively spliced isoform of ALG-2 (ALG-2ΔGF122) and structural roles of F122 in target recognition
Authors: Inuzuka Tatsutoshi , Suzuki Hironori , Kawasaki Masato , Shibata Hideki , Wakatsuki Soichi , Maki Masatoshi 
Published: December 2019

Abstract

Background
ALG-2 (a gene product of PDCD6) belongs to the penta-EF-hand (PEF) protein family and Ca2+-dependently interacts with various intracellular proteins including mammalian Alix, an adaptor protein in the ESCRT system. Our previous X-ray crystal structural analyses revealed that binding of Ca2+ to EF3 enables the side chain of R125 to move enough to make a primary hydrophobic pocket (Pocket 1) accessible to a short fragment of Alix. The side chain of F122, facing a secondary hydrophobic pocket (Pocket 2), interacts with the Alix peptide. An alternatively spliced shorter isoform, designated ALG-2ΔGF122, lacks Gly121Phe122 and does not bind Alix, but the structural basis of the incompetence has remained to be elucidated.

Results
We solved the X-ray crystal structure of the PEF domain of ALG-2ΔGF122 in the Ca2+-bound form and compared it with that of ALG-2. Deletion of the two residues shortened α-helix 5 (α5) and changed the configuration of the R125 side chain so that it partially blocked Pocket 1. A wall created by the main chain of 121-GFG-123 and facing the two pockets was destroyed. Surprisingly, however, substitution of F122 with Ala or Gly, but not with Trp, increased the Alix-binding capacity in binding assays. The F122 substitutions exhibited different effects on binding of ALG-2 to other known interacting proteins, including TSG101 (Tumor susceptibility gene 101) and annexin A11. The X-ray crystal structure of the F122A mutant revealed that removal of the bulky F122 side chain not only created an additional open space in Pocket 2 but also abolished inter-helix interactions with W95 and V98 (present in α4) and that α5 inclined away from α4 to expand Pocket 2, suggesting acquirement of more appropriate positioning of the interacting residues to accept Alix.

Conclusions
We found that the inability of the two-residue shorter ALG-2 isoform to bind Alix is not due to the absence of bulky side chain of F122 but due to deformation of a main-chain wall facing pockets 1 and 2. Moreover, a residue at the position of F122 contributes to target specificity and a smaller side chain is preferable for Alix binding but not favored to bind annexin A11.
Improved method for protein complex detection using bottleneck proteins
Authors: Ahn Jaegyoon , Lee Dae Hyun , Yoon Youngmi , Yeu Yunku , Park Sanghyun 
Published: December 2019

Abstract

Background
Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable.

Methods
For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other.

Results
We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection.

Conclusions
Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.
HDAM: a resource of human disease associated mutations from next generation sequencing studies
Authors: Jia Meiwen , Liu Yanli , Shen Zhongchao , Zhao Chen , Zhang Meixia , Yi Zhenghui , Wen Chengping , Deng Youping , Shi Tieliu 
Published: December 2019

Abstract

Background
Next generation sequencing (NGS) technologies have greatly facilitated the rapid and economical detection of pathogenic mutations in human disorders. However, mutation descriptions are hard to be compared and integrated due to various reference sequences and annotation tools adopted in different articles as well as the nomenclature of diseases/traits.

Description
The Human Disease Associated Mutation (HDAM) database is dedicated to collect, standardize and re-annotate mutations for human diseases discovered by NGS studies. In the current release, HDAM contains 1,114 mutations, located in 669 genes and associated with 125 human diseases through literature mining. All mutation records have uniform and unequivocal descriptions of sequence changes according to the Human Genome Sequence Variation Society (HGVS) nomenclature recommendations. Each entry displays comprehensive information, including mutation location in genome (hg18/hg19), gene functional annotation, protein domain annotation, susceptible diseases, the first literature report of the mutation and etc. Moreover, new mutation-disease relationships predicted by Bayesian network are also presented under each mutation.

Conclusion
HDAM contains hundreds rigorously curated human mutations from NGS studies and was created to provide a comprehensive view of these mutations that confer susceptibility to the common disorders. HDAM can be freely accessed at http://www.megabionet.org/HDAM.