Hobotnica: exploring molecular signature quality

Stupnikov, Alexey; Sizykh, Alexey; Favorov, Alexander V.; Afsari, Bahman; Wheelan, Sarah J.; Marchionni, Luigi; Medvedeva, Yulia A.

doi:10.12688/f1000research.74846.1

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…To address these problems, we applied the Hobotnica metric (H-score) [20] that we previously developed to assess the quality of molecular signatures obtained by the differential analysis of two or more groups of samples with different phenotypic characteristics and validated for DGE and DM signatures. In this way, H-scores of different DM signatures may be compared, allowing the direct evaluation of the models' performance for a particular dataset by assessing the quality of phenotypes separation, delivered by a particular signature [21].…”

Section: Hobotnica Approachmentioning

confidence: 99%

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Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Budkina

Medvedeva

Stupnikov

2023

IJMS

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Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods’ performance and DM signatures’ quality in the absence of gold standard data solving a long-existing problem in DM analysis.

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Section: Hobotnica Approachmentioning

confidence: 99%

“…Hobotnica [20] evaluates signatures based on the distance values between samples, which is inferred as the distance between vectors from the molecular signature subset of molecular features (CpG site positions). For differential methylation analysis, each vector contains methylation level values.…”

Section: Hobotnicamentioning

confidence: 99%

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Budkina

Medvedeva

Stupnikov

2023

IJMS

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scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting

Akhtyamov,

Shaheen,

Raevskiy

et al. 2023

Briefings in Bioinformatics

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Single-cell ATAC-seq (scATAC-seq) is a recently developed approach that provides means to investigate open chromatin at single cell level, to assess epigenetic regulation and transcription factors binding landscapes. The sparsity of the scATAC-seq data calls for imputation. Similarly, preprocessing (filtering) may be required to reduce computational load due to the large number of open regions. However, optimal strategies for both imputation and preprocessing have not been yet evaluated together. We present SAPIEnS (scATAC-seq Preprocessing and Imputation Evaluation System), a benchmark for scATAC-seq imputation frameworks, a combination of state-of-the-art imputation methods with commonly used preprocessing techniques. We assess different types of scATAC-seq analysis, i.e. clustering, visualization and digital genomic footprinting, and attain optimal preprocessing-imputation strategies. We discuss the benefits of the imputation framework depending on the task and the number of the dataset features (peaks). We conclude that the preprocessing with the Boruta method is beneficial for the majority of tasks, while imputation is helpful mostly for small datasets. We also implement a SAPIEnS database with pre-computed transcription factor footprints based on imputed data with their activity scores in a specific cell type. SAPIEnS is published at: https://github.com/lab-medvedeva/SAPIEnS. SAPIEnS database is available at: https://sapiensdb.com

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Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking

Bezuglov

Stupnikov

Skakov

et al. 2023

IJMS

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Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length − 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts.

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Hobotnica: exploring molecular signature quality

Cited by 3 publications

References 18 publications

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting

Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking

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