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Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.

Fruits produce a wide variety of secondary metabolites of great economic value. Analytical measurement of the metabolites is tedious, time-consuming, and expensive. Additionally, metabolite concentrations vary greatly from tree to tree, making it difficult to choose trees for fruit collection. The current study tested whether deep learning-based models can be developed using fruit and leaf images alone to predict a metabolite's concentration class (high or low). We collected fruits and leaves (n = 1045) from neem trees grown in the wild across 0.6 million sq km, imaged them, and measured concentration of five metabolites (azadirachtin, deacetyl-salannin, salannin, nimbin and nimbolide) using high-performance liquid chromatography. We used the data to train deep learning models for metabolite class prediction. The best model out of the seven tested (YOLOv5, GoogLeNet, InceptionNet, EfficientNet_B0, Resnext_50, Resnet18, and SqueezeNet) provided a validation F1 score of 0.93 and a test F1 score of 0.88. The sensitivity and specificity of the fruit model alone in the test set were 83.52 ± 6.19 and 82.35 ± 5.96, and 79.40 ± 8.50 and 85.64 ± 6.21, for the low and the high classes, respectively. The sensitivity was further boosted to 92.67± 5.25 for the low class and 88.11 ± 9.17 for the high class, and the specificity to 100% for both classes, using a multi-analyte framework. We incorporated the multi-analyte model in an Android mobile App Fruit-In-Sight that uses fruit and leaf images to decide whether to 'pick' or 'not pick' the fruits from a specific tree based on the metabolite concentration class. Our study provides evidence that images of fruits and leaves alone can predict the concentration class of a secondary metabolite without using expensive laboratory equipment and cumbersome analytical procedures, thus simplifying the process of choosing the right tree for fruit collection.

Purpose: Accurate detection of human papillomavirus (HPV) in oral cavity squamous cell carcinoma (OSCC) is essential to understanding the role of HPV in disease prognosis and management of patients. We used different analytes and methods to understand the true prevalence of HPV in a cohort of patients with OSCC with different molecular backgrounds, and we correlated HPV data with patient survival. Methods: We integrated data from multiple analytes (HPV DNA, HPV RNA, and p16), assays (immunohistochemistry, polymerase chain reaction [PCR], quantitative PCR [qPCR], and digital PCR), and molecular changes (somatic mutations and DNA methylation) from 153 patients with OSCC to correlate p16 expression, HPV DNA, and HPV RNA with HPV incidence and patient survival. Results: High prevalence (33% to 58%) of HPV16/18 DNA did not correlate with the presence of transcriptionally active viral genomes (15%) in tumors. Eighteen percent of the tumors were p16 positive and only 6% were both HPV DNA and HPV RNA positive. Most tumors with relatively high copy number HPV DNA and/or HPV RNA, but not with HPV DNA alone (irrespective of copy number), were wild-type for TP53 and CASP8 genes. In our study, p16 protein, HPV DNA, and HPV RNA, either alone or in combination, did not correlate with patient survival. Nine HPV-associated genes stratified the virus-positive from the virus-negative tumor group with high confidence (P < .008) when HPV DNA copy number and/or HPV RNA were considered to define HPV positivity, and not HPV DNA alone, irrespective of copy number (P < .2). Conclusion: In OSCC, the presence of both HPV RNA and p16 is rare. HPV DNA alone is not an accurate measure of HPV positivity and therefore may not be informative. HPV DNA, HPV RNA, and p16 do not correlate with patients’ outcome.

Tumor suppression by the extracts of Azadirachta indica (neem) works via anti-proliferation, cell cycle arrest, and apoptosis, demonstrated previously using cancer cell lines and live animal models. However, very little is known about the molecular targets and pathways that neem extracts and their associated compounds act through. Here, we address this using a genome-wide functional pooled shRNA screen on head and neck squamous cell carcinoma cell lines treated with crude neem leaf extracts, known for their anti-tumorigenic activity. We analyzed differences in global clonal sizes of the shRNA-infected cells cultured under no treatment and treatment with neem leaf extract conditions, assayed using next-generation sequencing. We found 225 genes affected the cancer cell growth in the shRNA-infected cells treated with neem extract. Pathway enrichment analyses of whole-genome gene expression data from cells temporally treated with neem extract revealed important roles played by the TGF-β pathway and HSF-1-related gene network. Our results indicate that neem extract affects various important molecular signaling pathways in head and neck cancer cells, some of which may be therapeutic targets for this devastating tumor.

Neem (Azadirachta indica A. Juss.), an evergreen tree of the Meliaceae family, is known for its medicinal, cosmetic, pesticidal and insecticidal properties. We had previously sequenced and published the draft genome of a neem plant, using mainly short read sequencing data. In this report, we present an improved genome assembly generated using additional short reads from Illumina and long reads from Pacific Biosciences SMRT sequencer. We assembled short reads and error-corrected long reads using Platanus, an assembler designed to perform well for heterozygous genomes. The updated genome assembly (v2.0) yielded 3- and 3.5-fold increase in N50 and N75, respectively; 2.6-fold decrease in the total number of scaffolds; 1.25-fold increase in the number of valid transcriptome alignments; 13.4-fold less misassembly and 1.85-fold increase in the percentage repeat, over the earlier assembly (v1.0). The current assembly also maps better to the genes known to be involved in the terpenoid biosynthesis pathway. Together, the data represent an improved assembly of the A. indica genome.

Selection of the right reference gene(s) is crucial in the analysis and interpretation of gene expression data. The aim of the present study was to discover and validate a minimal set of internal control genes in head and neck tumor studies. We analyzed data from multiple sources (in house whole-genome gene expression microarrays, previously published quantitative real-time PCR (qPCR) data and RNA-seq data from TCGA) to come up with a list of 18 genes (discovery set) that had the lowest variance, a high level of expression across tumors, and their matched normal samples. The genes in the discovery set were ranked using four different algorithms (BestKeeper, geNorm, NormFinder, and comparative delta Ct) and a web-based comparative tool, RefFinder, for their stability and variance in expression across tissues. Finally, we validated their expression using qPCR in an additional set of tumor:matched normal samples that resulted in five genes (RPL30, RPL27, PSMC5, MTCH1, and OAZ1), out of which RPL30 and RPL27 were most stable and were abundantly expressed across the tissues. Our data suggest that RPL30 or RPL27 in combination with either PSMC5 or MTCH1 or OAZ1 can be used as a minimal set of control genes in head and neck tumor gene expression studies.

Availability of snake genome sequences has opened up exciting areas of research on comparative genomics and gene diversity. One of the challenges in studying snake genomes is the acquisition of biological material from live animals, especially from the venomous ones, making the process cumbersome and time-consuming. Here, we report comparative sequence analyses of putative toxin gene homologs from Russell’s viper (Daboia russelii) using whole-genome sequencing data obtained from shed skin. When compared with the major venom proteins in Russell’s viper studied previously, we found 45–100% sequence similarity between the venom proteins and their putative homologs in the skin. Additionally, comparative analyses of 20 putative toxin gene family homologs provided evidence of unique sequence motifs in nerve growth factor (NGF), platelet derived growth factor (PDGF), Kunitz/Bovine pancreatic trypsin inhibitor (Kunitz BPTI), cysteine-rich secretory proteins, antigen 5, andpathogenesis-related1 proteins (CAP) and cysteine-rich secretory protein (CRISP). In those derived proteins, we identified V11 and T35 in the NGF domain; F23 and A29 in the PDGF domain; N69, K2 and A5 in the CAP domain; and Q17 in the CRISP domain to be responsible for differences in the largest pockets across the protein domain structures in crotalines, viperines and elapids from the in silico structure-based analysis. Similarly, residues F10, Y11 and E20 appear to play an important role in the protein structures across the kunitz protein domain of viperids and elapids. Our study highlights the usefulness of shed skin in obtaining good quality high-molecular weight DNA for comparative genomic studies, and provides evidence towards the unique features and evolution of putative venom gene homologs in vipers.

Oral tongue squamous cell carcinomas (OTSCC) are a homogeneous group of tumors characterized by aggressive behavior, early spread to lymph nodes and a higher rate of regional failure. Additionally, the incidence of OTSCC among younger population (

Researchers interested in studying and constructing transcriptomes, especially for non-model species, face the conundrum of choosing from a number of available de novo and genome-guided assemblers. None of the popular assembly tools in use today achieve requisite sensitivity, specificity or recovery of full-length transcripts on their own. Here, we present a comprehensive comparative study of the performance of various assemblers. Additionally, we present an approach to combinatorially augment transciptome assembly by using both de novo and genome-guided tools. In our study, we obtained the best recovery and most full-length transcripts with Trinity and TopHat1-Cufflinks, respectively. The sensitivity of the assembly and isoform recovery was superior, without compromising much on the specificity, when transcripts from Trinity were augmented with those from TopHat1-Cufflinks.

Copy Number Alterations (CNAs) such as deletions and duplications; compose a larger percentage of genetic variations than single nucleotide polymorphisms or other structural variations in cancer genomes that undergo major chromosomal re-arrangements. It is, therefore, imperative to identify cancer-specific somatic copy number alterations (SCNAs), with respect to matched normal tissue, in order to understand their association with the disease. We have devised an accurate, sensitive, and easy-to-use tool, COPS, COpy number using Paired Samples, for detecting SCNAs. We rigorously tested the performance of COPS using short sequence simulated reads at various sizes and coverage of SCNAs, read depths, read lengths and also with real tumor:normal paired samples. We found COPS to perform better in comparison to other known SCNA detection tools for all evaluated parameters, namely, sensitivity (detection of true positives), specificity (detection of false positives) and size accuracy. COPS performed well for sequencing reads of all lengths when used with most upstream read alignment tools. Additionally, by incorporating a downstream boundary segmentation detection tool, the accuracy of SCNA boundaries was further improved. Here, we report an accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied. COPS is available at ftp://115.119.160.213 with username "cops" and password "cops".

The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.

Abstract  Change of plant type in rice resulting in increased compactness of the panicle, allows space for accommodation of a larger number of spikelets, but grain yield does not increase proportionately because of limitations in grain filling. The objective of this study was to evaluate potential causes of poor filling of spikelets by comparing the physiological processes that influence source and sink activities between a compact- (OR-1920-7) and a loose-panicled (Lalat) rice cultivars growing in the open field conditions in the farm of Regional Research and Technology Transfer Station, Orissa University of Agriculture and Technology, Chiplima, India during dry season of 2007. Although grain number per unit length of the branches was higher in the compact-panicled cultivar than the loose-panicled cultivar, average grain weight was lower on the primary and secondary branches at top, middle and basal positions of the panicle in the former compared to the corresponding positions of the panicle in the latter. Compared to Lalat, ethylene production rate was considerably higher in the boot of the flag leaf sheath of OR-1920-7 during the pre-anthesis period. Ethylene evolution rate correlated negatively with growth and cell division rates and starch concentration of the juvenile endosperm. Because spikelet growth was slower in OR-1920-7 than in Lalat, unused carbohydrates accumulated in the endosperm. The stomatal conductance of the flag leaf during this period was also lower in the former than that of the latter and it correlated negatively with ethylene evolution rate of the boot. It is concluded that high ethylene production slackened grain filling of compact-panicled rice cultivar OR-1920-7 because of its adverse influence on both source and sink activities. [ABSTRACT FROM AUTHOR]