Adapter Trimming Research Articles

Improved characterization of single-cell RNA-seq libraries with paired-end avidity sequencing.

Prevailing poly(dT)-primed 3' single-cell RNA-seq protocols generate barcoded cDNA fragments containing the reverse transcriptase priming site, which is expected to be the poly(A) tail or a genomic adenine homopolymer. Direct sequencing across this priming site was historically difficult because of DNA sequencing errors induced by the homopolymeric primer at the 'barcode' end. Here, we evaluate the capability of "avidity base chemistry" DNA sequencing from Element Biosciences to sequence through this homopolymer accurately, and the impact of the additional cDNA sequence on read alignment and precise quantification of polyadenylation site usage. We find that the Element Aviti instrument sequences through the thymine homopolymer into the subsequent cDNA sequence without detectable loss of accuracy. The resulting paired-end alignments enable direct and independent assignment of reads to polyadenylation sites, which bypasses complexities and limitations of conventional approaches but does not consistently improve read mapping rates compared to single-end alignment. We also characterize low-level artifacts and arrive at an adjusted adapter trimming and alignment workflow that significantly improves the alignment of sequence data from Element and Illumina, particularly in the context of extended read lengths. Our analyses confirm that Element avidity sequencing is an effective alternative to Illumina sequencing for standard single-cell RNA-seq, particularly for polyadenylation site analyses but do not rule out the potential for similar performance from other emerging platforms.

An adaptive trimming approach to Bayesian additive regression trees

A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major challenge in forecasting. This study proposes an adaptive trimmed regression method using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Through extensive simulations across various scenarios, the effectiveness of BART(Atr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber’s loss function, and data-driven robust regression with Huber’s loss function. The simulation results consistently show BART(Atr) outperforming the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regression analysis example. Furthermore, random attack templates are introduced on the dataset to assess BART(Atr)’s performance under such conditions.

Negligible effects of read trimming on the accuracy of germline short variant calling in the human genome

Background Next generation sequencing (NGS) has become a standard tool in the molecular diagnostics of Mendelian disease, and the precision of such diagnostics is greatly affected by the accuracy of variant calling from sequencing data. Recently, we have comprehensively evaluated the performance of multiple variant calling pipelines. However, no systematic analysis of the effects of read trimming on variant discovery with modern variant calling software has yet been performed. Methods In this work, we systematically evaluated the effects of adapters on the performance of 8 variant calling and filtering methods using 14 standard reference Genome-in-a-Bottle (GIAB) samples. Variant calls were compared to the ground truth variant sets, and the effect of adapter trimming with different tools was assessed using major performance metrics (precision, recall, and F1 score). Results We show that adapter trimming has no effect on the accuracy of the best-performing variant callers (e.g., DeepVariant) on whole-genome sequencing (WGS) data. For whole-exome sequencing (WES) datasets subtle improvement of accuracy was observed in some of the samples. In high-coverage WES data (~200x mean coverage), adapter removal allowed for discovery of 2-4 additional true positive variants in only two out of seven datasets tested. Moreover, this effect was not dependent on the median insert size and proportion of adapter sequences in reads. Surprisingly, the effect of trimming on variant calling was reversed when moderate coverage (~80-100x) WES data was used. Finally, we show that some of the recently developed machine learning-based variant callers demonstrate greater dependence on the presence of adapters in reads. Conclusions Taken together, our results indicate that adapter removal is unnecessary when calling germline variants, but suggest that preprocessing methods should be carefully chosen when developing and using machine learning-based variant analysis methods.

Abstract PO4-23-11: Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing

Abstract Background Understanding the mechanisms of resistance to CDK4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is pivotal in exploring new therapeutic strategies for hormone receptor-positive (HR+), HER2-negative metastatic breast cancer. To decipher these resistance mechanisms, we analyzed the alterations in comprehensive RNA-seq (coding and non-coding RNAs) of HR+ HER2-negative BC cell lines via thermostable group II intron reverse transcriptase sequencing (TGIRT-seq). Methods We established Tamoxifen-resistant (TMR), Abemaciclib-resistant (ACR), Palbociclib-resistant (PCR), Tamoxifen/Abemaciclib double-resistant (TMR-ACR), and Tamoxifen/Palbociclib double-resistant (TMR-PCR) BC cell lines from MCF7 and T47D HR+/HER2- BC cell lines through stepwise dose-escalation continuous drug exposure. We performed a TGIRT-seq transcriptomic analysis using a protocol that allows the sequencing of both long and short non-coding and protein-coding RNAs in a single library. All libraries were sequenced using paired-end 150 bp on the Novaseq platform, resulting in an average of 50 million reads per library. For analysis, raw reads underwent adapter trimming, small RNA mapping, whole genome mapping, and the generation of annotated genes' read count. We used raw counts to detect differentially expressed genes (DEGs) with DESeq2 in R using the cut-off (log2 [fold change] > 1, FDR < 1e-3). We then used the selected DEGs for Gene Set Enrichment Analysis (GSEA) and Kaplan-Meier survival analysis from the TCGA database. Results The TGIRT-seq analysis identified 1171 to 3472 DEGs in different drug resistance and cell line combinations. Most DEGs in resistant cells were consistently downregulated compared to parent cells. Seventy-five percent of DEGs were protein-coding genes, with the rest being non-coding RNAs, such as small nucleolar RNAs, microRNAs, long non-coding RNAs, and transposable element RNAs, which conventional RNA-seq poorly detects. In principal component analysis (PCA) plots, replicates per drug resistance clustered clearly in both cell line backgrounds. PCA also indicated distinctive pathways for acquiring drug resistance in MCF7 and T47D cells. The estrogen receptor (ER) expression decreased, while HER2 increased in resistant cell lines. DEGs identified were enriched in ESR1-related pathways in all MCF7 and T47D cell lines resistant models. Several ER-regulating genes like IL-1R1 and RET were upregulated, while ADCY1 was consistently downregulated across different resistance types. A significant overlap with single-resistance DEGs was observed among the DEGs in double-resistance cell lines, but 20-50% of up/down-regulated DEGs in double-resistance cell lines were uniquely altered. Several previously identified DEGs (e.g., SALL4, TOP2A) and 21 novel candidate genes correlated with poor survival outcomes. Conclusion The analysis identified unique DEGs in double resistance cell lines, suggesting that double resistance mechanisms may not merely be a cumulative effect of single resistance mechanisms, necessitating further investigation and validation. CDK4/6i and/or ET-resistant BC cell lines displayed significant transcriptome reprogramming during the development of drug resistance. In previously published research, ESR1-related pathway alterations were proposed in tamoxifen resistance cell lines. Here we find that CDK4/6i-resistant cells also modify these ESR1-related pathways. Additionally, we identified several targetable genes, such as IL-1R1 and RET, involved in ESR1-related pathways that could pave the way for developing new treatment strategies. Citation Format: Toshiaki Iwase, Hengyi Xu, Nakyung Oh, Jangsoon Lee, Alan M Lambowitz, Naoto Ueno. Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-23-11.

FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing.

Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines.

Benchmarking software tools for trimming adapters and merging next-generation sequencing data for ancient DNA.

Ancient DNA is highly degraded, resulting in very short sequences. Reads generated with modern high-throughput sequencing machines are generally longer than ancient DNA molecules, therefore the reads often contain some portion of the sequencing adaptors. It is crucial to remove those adaptors, as they can interfere with downstream analysis. Furthermore, overlapping portions when DNA has been read forward and backward (paired-end) can be merged to correct sequencing errors and improve read quality. Several tools have been developed for adapter trimming and read merging, however, no one has attempted to evaluate their accuracy and evaluate their potential impact on downstream analyses. Through the simulation of sequencing data, seven commonly used tools were analyzed in their ability to reconstruct ancient DNA sequences through read merging. The analyzed tools exhibit notable differences in their abilities to correct sequence errors and identify the correct read overlap, but the most substantial difference is observed in their ability to calculate quality scores for merged bases. Selecting the most appropriate tool for a given project depends on several factors, although some tools such as fastp have some shortcomings, whereas others like leeHom outperform the other tools in most aspects. While the choice of tool did not result in a measurable difference when analyzing population genetics using principal component analysis, it is important to note that downstream analyses that are sensitive to wrongly merged reads or that rely on quality scores can be significantly impacted by the choice of tool.

First report of Pepper yellow dwarf strain of Beet curly top virus and Spinach curly top Arizona virus in red table beet in Idaho, United States.

In the fall 2021, red table beet plants (Beta vulgaris L. cv 'Eagle') exhibiting stunted growth with shorter petioles were observed at an incidence of 10 to 15 percent in a production field in Payette County, Idaho, United States. In addition to stunting, beet leaves displayed yellowing and mild curling and crumpling, and the roots exhibited hairy root symptoms (sFig.1). To identify potential causal viruses, total RNA was isolated from the leaf and root tissue using RNeasy Plant Mini Kit (Qiagen, Valencia, CA) and subjected to high-throughput sequencing (HTS). Two libraries were prepared, one for the leaf sample and another for the root sample using a ribo-minus TruSeq Stranded Total RNA Library Prep kit (Illumina, San Diego, CA). HTS was performed with 150 bp paired-end sequencing on a NovaSeq 6000 (Novogene, Sacramento, CA). Following adapter trimming and removal of host transcripts, 5.9 and 16.2 million reads were obtained from the leaf and root samples, respectively. These reads were de novo assembled using the SPAdes assembler (Bankevitch et al., 2012; Prjibelski et al., 2020). The assembled leaf sample contigs were aligned to the NCBI non-redundant database to identify contigs matching known viruses. A single contig of 2845 nts that shared 96% coverage and 95.6% sequence identity to the pepper yellow dwarf strain of beet curly top virus (BCTV-PeYD, EU921828; Varsani et al., 2014), and 98% coverage and 98.39% identity with an isolate of BCTV-PeYD (KX529650) from Mexico, was identified in the leaf sample (GenBank Accession OP477336). To validate the HTS detection of BCTV-PeYD, total DNA was isolated from the leaf sample and a 454 bp fragment of the C1 gene (replication-associate protein) was PCR amplified and Sanger sequencing of the amplicon revealed 99.7% identity to the HTS assembled BCTV-PeYD sequence. In addition to the PeYD strain of BCTV, the Worland strain of BCTV (BCTV-Wor) was detected as a single 2930 nt contig with 100% coverage and 97.3% identity to the BCTV-Wor isolate CTS14-015 (KX867045) known to infect sugar beet in Idaho. Of note, there are 11 strains of BCTV and among those, the BCTV-Wor strain induces mild symptoms in sugar beet (Strausbaugh et al., 2017), whereas BCTV-PeYD was found only in pepper from New Mexico. Further, two contigs of 2201 nts and 523 nts were assembled generating a nearly complete genome of spinach curly top Arizona virus (SpCTAV) in the leaf sample with 99% coverage and 99.3% identity (GenBank Accession OQ703946) to the reference genome of SpCTAV (HQ443515; Hernandez-Zepeda et al., 2013). To validate the HTS results, total DNA was isolated from the leaf tissue and PCR amplified a 442 bp fragment that overlaps the V1, V2, and V3 ORFs and its sequence revealed 100% identity with the HTS assembled SpCTAV. The roots sample also showed HTS reads corresponding to BCTV-PeYD and SpCTAV. In addition, beet necrotic yellow vein virus (BNYVV) was detected in the root sample with 30% coverage, but no sequence reads matching to BNYVV was detected in the leaf sample. BNYVV is known to infect sugar beet causing rhizomania (Tamada et al., 1973; Schirmer et al., 2005). To further confirm the BNYVV HTS results, total RNA was extracted separately from the root and leaf tissue, and RT-PCR was performed with primers that were designed to amplify portions of BNYVV RNAs (Weiland et al., 2020). RT-PCR analysis generated the appropriate amplicons with expected sequences corresponding to the RNA-1, RNA-2, RNA-3, and RNA-4 of BNYVV as determined by Sanger sequencing implying BNYVV the causal agent of hairy root symptoms. Similar to observations seen for BNYVV infection in conventional sugar beet varieties, no amplification was detected for BNYVV in the RNA extracted from leaf tissue, indicating that the RT-PCR results are consistent with the HTS analysis. This is the first report of BCTV-PeYD and SpCTAV observed naturally infecting red table beet in Idaho suggesting the geographical expansion of these viruses. The co-existence of BCTV-PeYD and SpCTAV with limited host range needs to be investigated to determine the actual cause of the observed foliar symptoms. This report provides the basis for further research to understand the pathogenic nature of these viruses and their potential threat to red table beet and sugar beet production in Idaho.

First report of Passion fruit green spot virus in yellow Passion fruit (Passiflora edulis f. flavicarpa) in Casanare, Colombia.

Passiflora edulis, commonly known as passion fruit, is a vine species of passionflower native to South America. In Colombia, yellow passion fruit (P. edulis f. flavicarpa) is the most important species in terms of net production and local consumption. Recently two brevipalpus transmitted cileviruses, (i) passion fruit green spot virus (PfGSV) and (ii) hibiscus strain of citrus leprosis virus C2 (CiLV-C2H) were detected in passion fruit in Brazil and Hawaii, respectively (Ramos-González et al., 2020, Olmedo-Velarde et al., 2022). CiLV-C2H infects both citrus and hibiscus in Colombia (Roy et al., 2015, 2018) but there was no report of PfGSV elsewhere apart from Brazil and Paraguay (Costa-Rodrigues et al., 2022). Apart from emerging begomovirus diseases, five major viruses are known to infect passion fruit in Colombia: soybean mosaic virus (SMV), cowpea aphid-borne mosaic virus, passion fruit yellow mosaic virus, cucumber mosaic virus, and a tentative Gulupa bacilliform badnavirus A (Cardona et al., 2022). Current findings of CiLV-C2H in passion fruit and PfGSV in hibiscus motivated us to investigate the possibilities of cilevirus infection in passion fruit in Colombia. During surveys, along with healthy yellow passion fruit leaves, five symptomatic plant samples from Meta and three from Casanare were collected before sent to the Molecular Plant Pathology Laboratory at Beltsville, MD under APHIS permit. Passion fruit samples from Meta showed leaf mottling, rugose mosaic, and leaf distortion, whereas leaf variegation, chlorotic spots, yellowing, green spots in senescent leaves and green vein banding were observed in the Casanare samples (Supp. Fig. 1). Total RNA was extracted using RNeasy Plant Mini Kit (Qiagen, USA). To know the potential cilevirus infection in these samples, three PfGSV specific (Ramos-González et al. 2020) and a CiLV-C2 generic primer pairs (Olmedo-Velarde et al. 2021) were used in the RT-PCR assays. All five passion fruit samples from Meta failed to produce either CiLV-C2 or CiLV-C2H or PfGSV amplicon whereas all three Casanare samples successfully amplified 321, 244 and 299 nts of PfGSV-RNA1 and -RNA2 amplicons using C13F/C13R, C6F/C6R and C8F/C8R primers, respectively. Bi-directional amplicon sequencing followed by BlastN analysis revealed ≥99% nt identity with the PfGSV-RNA1 (MK804173) and -RNA2 (MK804174) genome sequences. An optimized ribo-depleted library preparation protocol was utilized to prepare two cDNA libraries using the RNA extracts of a PfGSV suspected positive (Casanare) and a negative (Meta) samples (Chellappan et al., 2022). HTS libraries of Casanare and Meta samples resulted in 22.7 to 29.5 million raw reads, respectively. After adapter trimming and filtering, clean reads were mapped to the Arabidopsis thaliana reference genome and unmapped reads were de novo assembled (Chellappan et al., 2022). BlastN analysis from the assembled contigs identified 1-3 contigs corresponding to PfGSV-RNA1 and -RNA2, respectively, from Casanare sample whereas 3 contigs of SMV were identified in Meta passion fruit sample. No other virus sequence was obtained from either of the libraries. Assembled contigs covered 99.33% of the RNA1 and 94.42% of the RNA2 genome, with read depths of 64,474 and 119,549, respectively. Meta sample contigs (OP564897) covered >99% of the SMV genome, which shared >99% nt identity with the Colombian SMV isolates (KY249378, MW655827). Both RNA-1 (OP564895) and -2 (OP564896) segments of the Casanare isolate shared 99% nt identity with PfGSV isolate (MK804173-74). Our discovery identified PfGSV in Colombia, for the first-time outside Brazil and Paraguay. The findings of PfGSV in yellow passion fruit increases the potential threat and possibility of PfGSV movement via Brevipalpus sp. from passion fruit to other hosts.

Systematic Assessment of Small RNA Profiling in Human Extracellular Vesicles.

Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs.

Association between intra-tumoral microbiome and clinical benefit (CB) from immunotherapy (IO) in patients with metastatic renal cell carcinoma (mRCC).

4561 Background: Specific bacteria and microbial diversity in the gut may drive response to IO in patients (pts) with mRCC (Routy et al Science 2018; Salgia et al Eur Urol 2020). Despite the evidence of bacterial proliferation in RCC tumors (Heidler et al Urol Int 2020), the impact of the intra-tumoral microbiome in RCC has been poorly delineated. Here, we explored its association to CB in mRCC pts receiving IO-based regimens. Methods: We identified pts with mRCC and available RNA sequencing (RNA-seq) data from tumor tissue collected prior to start of IO.Intra-tumoral microbiome analysis was performed on formalin-fixed paraffin-embedded samples. Following quality and adapter trimming, RNA-seq reads were mapped to a human genome to filter host reads using the STAR alignment tool. Taxonomic classification was performed using Kraken2 and Bracken. Following removal of common contaminants, the relative abundances among all non-human species were calculated. Microbial diversity was assessed using the Shannon index and statistical analysis performed using a t-test. Relative microbial abundance was compared between tumors from pts with or without CB from 1st line IO treatment using the Kruskal-Wallis test. P-values less than 0.05 were considered significant. Results: A total of 96 pts (71:25, M:F) with mRCC were included in this analysis. Of these, 85 (89%) had clear cell histology and 68 (71%) were IMDC intermediate/poor risk. The median age at the time of tissue sampling was 62.7 yrs old and most of the samples analyzed (57%) were collected from the primary site. IO, alone or in combination, was received as 1st line treatment in 57 pts (59%), with the most common regimen being nivolumab/ipilimumab (47%). CB from 1st line IO was observed in 39 (68%) pts. Tumors from pts achieving CB from 1st line IO had elevated microbial diversity compared to tumors from pts without CB (p=0.01). Tumor samples from these pts also had a significantly higher relative abundance of several bacterial genera compared to tumors from pts without CB. These included Pasteurella, Escherichia, and Cloacibacterium (p<0.05). No significant differences in bacterial diversity were observed between CB and no-CB pts receiving IO as 2nd line or beyond. Conclusions: This is the largest study exploring the association between the intra-tumoral microbiome of mRCC and CB from IO. In line with gut microbiome studies, an increased intra-tumoral Shannon diversity index was also associated with CB to IO in our 1st line cohort, further suggesting that bacterial diversity could be a driver of response. Additionally, several genera associated with CB are particularly relevant. For instance, members of the Pasteurella genus have been implicated in the activation of c-myc, a proto-oncogene that has been shown to induce PD-L1 expression.

Genetic heterogeneity between paired primary and metastatic solid tumors and implications for neoantigen-based personalized cancer vaccines.

3126 Background: Neoantigen-based personalized cancer vaccines carry significant promise in treating solid malignancies. For the purpose of neoantigen prediction, questions remain on the suitability of the primary versus the metastatic tumor of an individual patient (pt). Here we performed an in-depth analysis of somatic variants of 45 pts with paired primary and metastatic solid tumors. Methods: Pts were enrolled in the Total Cancer Care protocol across the Oncology Research Information Exchange Network (ORIEN). Whole-exome sequencing (WES) of primary and metastatic tumor pairs was performed for 45 pts. These included head and neck (n = 8), renal cell carcinoma (n = 7), non-small cell lung cancer (n = 6), melanoma (n = 5), bladder (n = 3), sarcoma (n = 3), ovary (n = 3), esophageal (n = 2), colorectal (n = 2) and other singletons (n = 5). The data was analyzed through the ORIEN AVATAR Molecular Analysis Pipeline, which consisted of adapter trimming, single sample variant detection, somatic mutation detection and filtering, variant annotation, and filtering of variants from a panel of normal samples. In this analysis, we focused on somatic events that result in an in-frame alteration (such as missense, in-frame insertion/deletion) and out-of-frame protein-altering mutations (such as frameshifts, de novo start and nonstop gain). To assign the clones based on the variant allele frequency, we performed complete linkage clustering. Each cluster was then assigned based on the identified oncogenic driver variant. Results: For in-frame events, we noticed that bladder cancer, melanoma, and gynecological cancers shared close to 75% of the mutations between paired primary and metastatic cases. In contrast, esophageal, brain, and endometrial cancers had a low overlap (< 25%) of variants. For out-of-frame events, we found that these events tend to have a lower proportion of shared somatic variants between primary and metastasis than in-frame variants. Through a closer evaluation of the variant allele frequency in variants shared between primary and metastatic disease, we were able to track several cancer drivers, such as oncogenic drivers of BRAF V600E, RB1 R358*, NRAS G13R, KRAS G12A, and TP53 loss-of-function that were consistently present in paired primary and metastatic tumors. Here, we were able to assign the putative clone based on the driver event in 39 out of 45 pts. Conclusions: Our analysis demonstrates genetic variations that exist when comparing paired primary and metastatic tumors that appear to vary by histology. Variants are potentially undergoing negative selection supported by the preferential loss of out-of-frame events in metastatic tumors. Understanding the clonal structure will be key to neoantigen prediction for effective neoantigen-based vaccines. Additional steps in variant prioritization are ongoing and will be reported at the meeting.

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

AbstractIn actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR‐based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra‐class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi‐supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR‐based BAL‐ALGT (DRBAL‐ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid‐connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL‐ALGT method can achieve higher accuracy for fault diagnosis.

RNAlysis: analyze your RNA sequencing data without writing a single line of code

BackgroundAmong the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results.ResultsWe have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files (adapter trimming, alignment, and feature counting), through exploratory data analysis and data visualization, clustering analysis, and gene set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C.elegans nematodes. We note that the software applies equally to data obtained from any organism with an existing reference genome.ConclusionsRNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

Revealing the History and Mystery of RNA-Seq.

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly, the theoretical analysis shows that the traditional Adaboost method is overfitting in a noisy training set, which leads to the degradation of classification accuracy. To this end, the penalty factor constructed by the number of consecutive misclassifications of samples is used to reconstruct the sample weight assignment to prevent the classifier from over-focusing on noisy samples, and its reasonableness is demonstrated. Then, by comparing the penalty strength of the three different penalty factors proposed in this paper, a more reasonable penalty factor is selected. Meanwhile, in order to make the constructed model more in line with the actual requirements on training time consumption, the Adaboost algorithm with adaptive weight trimming (AWTAdaboost) is used in this paper, so the penalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained. Finally, PF_AWTAdaboost is experimentally validated against other traditional machine learning algorithms on credit card fraud datasets and other datasets. The results show that the PF_AWTAdaboost method has better performance, including detection accuracy, model recall and robustness, than other methods on the credit card fraud dataset. And the PF_AWTAdaboost method also shows excellent generalization performance on other datasets. From the experimental results, it is shown that the PF_AWTAdaboost algorithm has better classification performance.

Characterization of the expressed RNA variants from young patients with critical and non-critical SARS-CoV-2 infection

BackgroundSince the COVID-19 outbreak emerged, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved into variants with underlying mutations associated with increased transmissibility, potential escape from neutralizing antibodies, and disease severity. Although intensive research is ongoing worldwide to understand the emergence of SARS-CoV-2 variants, there is a lack of information on what constitutes the expressed RNA variants in critical and non-critical comorbidity-free young patients. The study sought to characterize the expressed RNA variants from young patients with critical and non-critical forms of SARS-CoV-2 infection.MethodologyThe bulk ribonucleic acid (RNA) sequencing data with the identifier GSE172114 were downloaded from the Gene Expression Omnibus (GEO) database. The study participants were divided into critical, n = 46, and non-critical, n = 23. FastQC version 0.11.9 and Cutadapt version 3.7 were used to assess the read quality and perform adapter trimming, respectively. Spliced Transcripts Alignment to a Reference (STAR) version 2.7.10a was used to align reads to the human (hg38) reference genome. Genome Analysis Tool Kit (GATK) best practice was followed to call variants using the rnavar pipeline, part of the nf-core pipelines.ResultsOur research demonstrates that critical and non-critical SARS-CoV-2-infected individuals are characterized by a unique set of expressed RNA variants. The expressed gene variants are enriched on the innate immune response, specifically neutrophil-mediated immune response. On the other hand, the expressed gene variants are involved in both innate and cellular immune responses.ConclusionDeeply phenotyped comorbidity-free young patients with critical and non-critical SARS-CoV-2 infection are characterized by a unique set of expressed RNA variants. The findings in this study can inform the patient classification process in health facilities globally when admitting young patients infected with SARS-CoV-2.

A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging.

The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the whale optimization algorithm (WOA) tuning the least-square support vector machine (LSSVM) and trimmed bagging. To be specific, the stimulated annealing algorithm (SA) was hybridized to the WOA to solve the local entrapment problem, and an adaptive trimming strategy is proposed to obtain the optimal trim portion for the trimmed bagging. In addition, inverse quote error (invQE) and cross-validation are employed to estimate the fitness better in training process. The maximum absolute measurement error caused by temperature decreased from to of full scale after being compensated by the proposed method. The analyses of experiments illustrate the ensemble hWOA-LSSVM based on improved trimmed bagging improves the precision and stability of F/T sensors and possesses the strengths of local search ability and better adaptability.

Investigating expressed RNA variants that are related to disease severity in SARS-CoV-2-infected patients with mild-to-severe disease

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be a significant public health challenge globally. SARS-CoV-2 is a novel virus, and the understanding of what constitutes expressed RNAseq variants in healthy, convalescent, severe, moderate, and those admitted to the intensive care unit (ICU) is yet to be presented. We characterize the different expressed RNAseq variants in healthy, severe, moderate, ICU, and convalescent individuals.Materials and methodsThe bulk RNA sequencing data with identifier PRJNA639275 were downloaded from Sequence Reads Archive (SRA). The individuals were divided into: (1) healthy, n = 34, moderate, n = 8, convalescent, n = 2, severe, n = 16, and ICU, n = 8. Fastqc version 0.11.9 and Cutadapt version 3.7 were used to assess the read quality and perform adapter trimming, respectively. STAR was used to align reads to the reference genome, and GATK best practice was followed to call variants using the rnavar pipeline, part of the nf-core pipelines.ResultsOur analysis demonstrated that different sets of unique RNAseq variants characterize convalescent, moderate, severe, and those admitted to the ICU. The data show that the individuals who recover from SARS-CoV-2 infection have the same set of expressed variants as the healthy controls. We showed that the healthy and SARS-CoV-2-infected individuals display different sets of expressed variants characteristic of the patient phenotype.ConclusionThe individuals with severe, moderate, those admitted to the ICU, and convalescent display a unique set of variants. The findings in this study will inform the test kit development and SARS-CoV-2 patients classification to enhance the management and control of SARS-CoV-2 infection in our population.

Association of intra-tumoral microbiome and response to immune checkpoint inhibitors (ICIs) in patients with metastatic renal cell carcinoma (mRCC).

372 Background: Multiple studies have suggested that the gut microbiome plays a modulatory role in ICI activity and that specific bacteria and/or cumulative microbial diversity may drive response in patients (pts) with mRCC (Routy et al Science 2018; Salgia et al Eur Urol 2020). Even though the tumor microenvironment has substantial bacterial proliferation (Heymann et al Cancer L. 2021), there is a paucity of data assessing the impact of intra-tumoral microbiota in response to ICI therapy. In this study, we sought to explore this association in pts with mRCC. Methods: Pts diagnosed with mRCC who had available RNA sequencing (RNA-seq) data collected in the course of routine clinical care and who were treated with ICIs were retrospectively identified.Intra-tumoral microbiome analysis was performed on formalin-fixed paraffin-embedded samples. Following quality and adapter trimming, RNA-seq reads were mapped to a human genome to filter host reads using the Burrows-Wheeler alignment (BWA) tool. Taxonomic classification was performed using Kraken2 and the absolute abundances of species were estimated using Bracken. The relative abundances among all non-human species were calculated. Statistical testing with Student’s t-test was performed to compare the relative abundance for all species seen within pts who responded to ICIs and those who did not. Results: Among the 28 pts (22:6, M:F) included in this analysis, 24 (86%) had clear cell histology and 20 (71%) were IMDC intermediate/poor risk. All of the samples were collected prior to starting treatment with ICIs and the majority of these (57%) were collected from the primary site. 11 pts (39%) received ICIs as first line treatment and 17 (61%) as second line. Clinical response was seen in 50% of pts included in the study and the most common rendered treatment was nivolumab (17 pts). In the overall cohort, Cutibacterium acne, Moraxella osloensis, and Pasteurella multocida had the highest relative abundances. Additionally, significant differences in relative abundances of specific bacteria were found between ICI responders and non-responders. Among these, Stenotrophomonas maltophilia (p = 0.037) and Corynebacterium sp. zg-917 (p = 0.035) had significantly higher relative abundances in pts who responded to ICIs. Conclusions: This is the first study evaluating the association between intra-tumoral microbiome and response to ICIs in pts with mRCC. Among bacteria associated with response, several have particular relevance – for instance, Corynebacterium spp. have been studied for decades as a possible adjunct to immunotherapeutic agents such as BCG. Efforts are ongoing to validate these findings in a larger cohort.

Atria: an ultra-fast and accurate trimmer for adapter and quality trimming.

With advances in next-generation sequencing, adapters attached to reads and low-quality bases directly and implicitly hinder downstream analysis. For example, they can produce false-positive single nucleotide polymorphisms (SNP), and generate fragmented assemblies. There is a need for a fast trimming algorithm to remove adapters precisely, especially in read tails with relatively low quality. Here, we present Atria, a trimming program that matches the adapters in paired reads and finds possible overlapped regions using a fast and carefully designed byte-based matching algorithm (O (n) time with O (1) space). Atria also implements multi-threading in both sequence processing and file compression and supports single-end reads. Compared with other trimmers, Atria performs favorably in various trimming and runtime benchmarks of both simulated and real data. We also provide a fast and lightweight byte-based matching algorithm, which can be used in various short-sequence matching applications, such as primer search and seed scanning before alignment.