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A New R Package for Categorizing Coding and Non-Coding Genes

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Systems Biology and Network Medicine: From Bench to Bedside

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04 January 2023

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04 January 2023

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Abstract
Previous studies demonstrate the critical importance of non-coding RNAs interfacing with chromatin-modifying machinery resulting in promoter-enhancer-based gene regulation and raise the possibility that many other enhancer-like RNAs may operate via similar mechanisms. Critically, more than 80% of the disease-linked variations identified in genome-wide studies are located in the non-coding regions of genomes, especially non-coding RNA, suggesting non-coding RNAs are relevant to disease. Thus, a critical path forward for understanding non-coding RNAs' role, especially long non-coding RNAs, is to understand the genomic regions' transcriptional regulation, especially non-coding regions. Here, we developed a user-friendly R package called SomaGene for studying and identifying enhancer-like non-coding RNAs with enriched somatic mutations in the cancer genome. SomaGene accepts different genomic variants (whole genome/exome somatic point mutations, structural variations, copy number variations) to identify those RNAs that significantly mutated in diseases (e.g., cancer). It then uses multiple publicly available genomics and epigenetics datasets including ENCODE epigenomics annotations, FANTOM5 tissue-specific expression profiles, disease-associated genome-wide association SNPs, and tissue-specific eQTL pairs to identify those RNAs with potentially enhancer function. SomaGene, as a powerful R package, can provide the opportunity to cancer scientists to study the roles of non-coding RNAs in different cancer genomes.
Keywords: 
Subject: Biology and Life Sciences  -   Biochemistry and Molecular Biology

1. Introduction

Over the past years, attempts to associate mutated genomic regions to cancer development have been preferentially focused on protein-coding genes as their functional structure is well studied. Nevertheless, most parts of the genome are non-coding regions, representing the vast majority of transcripts, even though not translated, appeared to perform significant roles in cancer and genetic diseases, and their contribution to tumor initiation and progression has been known by genome-wide association and whole genome/exome studies (1-9). However, despite extensive studies on discovering the relationship between DNA sequence and genomic functions for protein-coding genes, this relationship for non-coding genomic regions is less understood. However, it is now well known that non-coding RNAs are able to contribute to various cellular or regulatory activities in the cell, such as regulating gene expression via interaction with other chromatin regulatory proteins (10) function as enhancers (11, 12), and regulate chromatin structure (13-16). Despite various studies on the impact of non-coding somatic mutations occurring in ncRNAs, the role of such non-coding RNAs has remained underexplored in cancer.
Moreover, the emerging of the next-generation sequencing technologies for identifying cancer driver mutations and cancer-associated genomic regions yields the comprehensive publicly available catalogs of mutations in various cancers provided by ICGC and TCGA consortia. The most widely used methods for prioritizing non-coding genes rely on modeling the mutation rates using mutational catalogs in cancer or viral genomes (6, 17-23). Although helping prioritize non-coding mutated regions such as Introns, UTRs, promoters, and mRNAs, the statistical methods modeling mutation rates fail to be fit for ncRNAs, in particular, long noncoding RNAs (lncRNA) as one of the most important classes of non-coding transcripts involved in many cellular processes which the length of these transcripts are highly variable (200 bp to 100 kb).
This paper introduces a new prioritization pipeline (Figure 1), which takes a set of genomic ranges (e.g., non-coding genes) and a comprehensive mutational catalog associated with various cancers. It provides a prioritized list of genomic ranges that are significantly and recurrently mutated in each cancer. Rather than focusing on modeling mutation rates at the nucleotide level, this method calculates a P-value for each genomic region (e.g., a non-coding gene) accounting to two factors: (1) the level of mutation recurrence in the region among samples and (2) the extent to which the mutations in the region are specific to the samples of the cancer of interest comparing to other cancers. This statistical test can be followed by annotating input genomic regions with various functional assays such as disease-related genomic variants and regulatory features (e.g., chromatin marks, ChromHMM predicted promoters enhancers). The whole pipeline is designed as an R package, SomaGene, to provide an integrative assessment of somatic mutations to obtain a list of candidate genomic regions potentially functional in developing cancer. SomaGene can analyze and prioritize any set of genomic regions such as coding and non-coding genes in cancers.

2. Implementation

Statistical framework

Let M = { m 1 ,   m 2 ,   … ,   m X } , be a set of mutational catalogs for X cancers, and R = r 1 ,   r 2 ,   … ,   r Y be the set of Y input genomic regions. Using all mutational catalogs, a p-value for the region r y in cancer x is calculated using a one-sided Fisher’s exact test with the following contingency table:
Table 1. Contingency table for Fisher’s exact test.
Table 1. Contingency table for Fisher’s exact test.
#samples in m x that have a mutation in the region r y . #samples in m x that do not have a mutation in the region r y .
#samples in all catalogs except m x that have a mutation in the region r y . #samples in all catalogs except m x that do not have a mutation in the region r y .
This Fisher’s exact test (which is applied for all regions of interest in R ) is followed by a permutation test (typically for 1,000,000 random permutations - can be changed by the user) to estimate the probability that any of the associations emerges by chance. In the permutation process, the order of sample IDs in the original mutational catalogs is shuffled to generate a list of simulated datasets of mutations. Then the Fisher’s exact test is applied to each simulated dataset to obtain a list of simulated P-values for each r y (the number of simulated P-values equals the number of permutations). Finally, each r y is determined to be significant if its original P-value (obtained from Fisher’s exact test on the original data) is less than the permutations P-value at confidence interval 99%. Users can define the significance level for the Fisher’s exact test and the confidence interval for the permutation test. Using this statistical framework, the user can extract a significant list of genes (from an initial list) that compared to other cancers, are recurrently and specifically mutated in cancer x .

Annotations scheme

Let R = { r 1 ,   r 2 ,   … ,   r Y } be the set of Y input genomic regions and A = { a 1 ,   a 2 ,   … ,   a Z } be a set of Z genomic annotation entries. A can be indicated as one of five general types of annotation (e.g., tissue-specific histone marks, encode predicated promoters and enhancers, GWAS, eQTL) that attribute specific genomic features to a set of genomic regions. This package aides the user to interpret an arbitrary set of regions R with an optional annotation A (one of five general types).
In the case of annotation of type 1, which is a simple catalog of genomic regions such as the list of active enhancers by FANTOM5 (24) or genomic positions of disease-associated genomic variants by GWAS Catalog (25), the annotation output is a list designating the occurrence of overlap/the number of overlaps, of annotation entries with each r y regions.
The 2nd type of annotation assigns a category to each genomic annotation entries a z , (e.g., the annotation of chromatin state segmentation (26)) and the result for each region r Y indicates the categories overlapping with r Y along with the percentages of overlaps.
In the case of 3rd annotation, a catalog assigns a single score to each annotation entry (such as the list of genomic regions enriched for histone modifications (27)) and the annotation result for each regions r Y will be an aggregated score and the percentage of its length that overlaps with annotation entries.
The 4th type of annotation assigns a group of IDs (which we call sub_ids) and their corresponding scores (which we call sub_scores) to each annotation entry (e.g., DNase clusters (28)). In this case, the output for each region r Y will represent the overlapping sub_ids and the corresponding aggregated sub_scores.
The 5th type annotation is defined for chromatin interaction datasets (such as Hi-C). In this case, the user provides two commentaries, one for chromatin interactions and one for target genomic regions, such as T = { t 1 ,   t 2 ,   … ,   t G } . As a result, the package will identify the interactions between entries of original genomic regions R and target genomic regions T (see supplementary materials for more details). For instance, the user can investigate the interactions between a set of lncRNAs (entries of R ) that interact with a group of protein-coding genes (entries of T ) through a dataset of Hi-C interactions in breast cancer. More details about the calculation of overlaps, scores, and sub_scores are explained in the supplementary materials.

3. Application of SomaGene

An application of SomaGene can be found in a recent comprehensive study aiming to prioritize non-coding RNA genes in the context of cancer Catalog (29). In this study, a list of 65,000 non-coding genes and the mutational catalogs of 19 cancer types downloaded from the ICGC consortia, were used as input to SomaGene for identifying those non-coding genes that significantly mutated in breast cancer samples. SomaGene identified 929 non-coding genes as significantly mutated genes in breast cancer samples (confidence interval 99% on 1,000,000 permutations provided by SomaGene). Interestingly, the candidate non-coding RNAs have significantly greater fraction of breast tissue related ENCODE enhancer and promoter marks (Figure 2a), FANTOM5 breast tissue differentially expressed enhancers (Figure 2b), ENCODE chromatin active histone marks (Figure 2c), and breast cancer associated genome-wide association SNPs (GWAS) (Figure 2d).
Also, we sorted non-coding RNAs based on their mutational p-value and repeated the enrichment analyses for the second, third and last set of non-coding RNAs (i.e., those non-coding RNAs that did not encompass any mutations). Interestingly, the enrichment for regulatory features have been also seen in the second and third lists of non-coding RNAs, but in much smaller fractions compared to the significant list of non-coding RNAs (Figure 2). As expected, no enrichment was seen for the last list of non-coding RNAs (Figure 2).
Finally, we used genomic and epigenetic annotations described in the above section and provided a prioritized list of non-coding RNAs with potential enhancer roles in breast cancer as well as five other cancers that can be accessed through our online resource http://ncrna.ictic.sharif.edu.
The medical and biological datasets are increasing rapidly. To analyse such big and complex data, artificial intelligence and integrative pipelines become most popular (5, 30-43). Here, we have developed SomaGene, a novel, user-friendly, interactive, open-access pipeline for identifying and annotating noncoding genes (or genomic coordinates) that significantly mutate in a cancer genome.

Supplementary Materials

Supplementary data are available at Bioinformatics online.

Author Contributions

HRR designed the study; MB, NR, MST, and HRR wrote and edited the manuscript with help from HAR. MB, NR, MH carried out all the analyses, including the statistical analyses, gene prioritization, annotation, and permutation under HAR and HRR supervision. MB generated all figures and tables under HRR supervision. All authors have read and approved the final version of the paper.

Data Availability Statement

The source code and a sample dataset can be accessed at https://github.com/bcb-sut/SomaGene. For more details about each parameter, please visit the https://github.com/bcb-sut/SomaGene/blob/master/README.md page.

Conflicts of Interest

The authors declare no competing financial interests.

Availability and Implementation

The SomaGene R package is freely accessible to the public at https://github.com/bcb-sut/SomaGene.

Tool Availability

The SomaGene source R package, a sample dataset, and instructions on how to run SomaGene are provided at https://github.com/bcb-sut/SomaGene and supplementary file entitled “user manual.”

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Figure 1. Flow diagram of SomaGene pipeline. SomaGene accepts a VCF file including genomic variants and a list of genomic coordinates. After counting the number of mutated samples in each non-coding RNA, it then uses Fisher’s exact test described in the method section to identify mutational P-value for each non-coding RNA. To determine the significantly mutated ncRNAs (can be also a genomic coordinate) for each ncRNA, SomaGene calculates P-values for 1,000,000 random permutations of disease/non-disease (for example breast/non-breast) labels to estimate the 99% C.I. threshold of P-values. SomaGene then investigates the overlapping of non-coding RNAs with cancertissue-related regulatory features (e.g., ENCODE predicted chromHMM, H3K27ac), cancer-related GWAS SNPs, eQTL polymorphisms, FANTOM5 promoters, enhancers and tissue/cell related Hi-C interacting regions (if available). The user has the option to use the existing annotation files in SomaGene or enter their annotation files in a standard bed format.
Figure 1. Flow diagram of SomaGene pipeline. SomaGene accepts a VCF file including genomic variants and a list of genomic coordinates. After counting the number of mutated samples in each non-coding RNA, it then uses Fisher’s exact test described in the method section to identify mutational P-value for each non-coding RNA. To determine the significantly mutated ncRNAs (can be also a genomic coordinate) for each ncRNA, SomaGene calculates P-values for 1,000,000 random permutations of disease/non-disease (for example breast/non-breast) labels to estimate the 99% C.I. threshold of P-values. SomaGene then investigates the overlapping of non-coding RNAs with cancertissue-related regulatory features (e.g., ENCODE predicted chromHMM, H3K27ac), cancer-related GWAS SNPs, eQTL polymorphisms, FANTOM5 promoters, enhancers and tissue/cell related Hi-C interacting regions (if available). The user has the option to use the existing annotation files in SomaGene or enter their annotation files in a standard bed format.
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Figure 2. Enrichment of regulatory features in the significant set of non-coding RNAs. a) Enrichment of breast-related promoters and enhancers identified by ENCODE (using chromatin segmentation by HMM). b) Enrichment of breast tissue differentially expressed enhancers identified by FANTOM5. c) Enrichemnt of ENCODE active histome marks. d) Enrichment of breast and lung cancer-associated GWAS SNPs. The enrichment is calculated by dividing the proportion of significantly mutated ncRNAs that overlap with each item by the proportion of all ncRNAs that overlap with that item. This enrichment is calculated for a significant set of ncRNAs (929 ncRNAs) shown in red color, 2nd set (blue), 3rd set (brown) of highly mutated ncRNAs. The enrichment was also calculated for the last set of ncRNAs that had no mutation in breast cancer samples. Each set of ncRNAs contains 929 elements.
Figure 2. Enrichment of regulatory features in the significant set of non-coding RNAs. a) Enrichment of breast-related promoters and enhancers identified by ENCODE (using chromatin segmentation by HMM). b) Enrichment of breast tissue differentially expressed enhancers identified by FANTOM5. c) Enrichemnt of ENCODE active histome marks. d) Enrichment of breast and lung cancer-associated GWAS SNPs. The enrichment is calculated by dividing the proportion of significantly mutated ncRNAs that overlap with each item by the proportion of all ncRNAs that overlap with that item. This enrichment is calculated for a significant set of ncRNAs (929 ncRNAs) shown in red color, 2nd set (blue), 3rd set (brown) of highly mutated ncRNAs. The enrichment was also calculated for the last set of ncRNAs that had no mutation in breast cancer samples. Each set of ncRNAs contains 929 elements.
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