Small RNA-seq analysis pipeline XICRA

https://github.com/cougarlj/COMPSRA/issues/18

https://github.com/HCGB-IGTP/XICRA

• This pipeline is designed to take paired end reads in fastq format, trim adapters and low-quality base pairs positions, and merge read pairs (R1 & R2) that overlap.
• A mapping step to the reference genome (user defined) assigns joined reads to all major RNA biotypes including miRNA and isomiRs, tRNA fragments (tRFs) and piwi associated RNAs (piRNAs).
• Then, XICRA produces a miRNA analysis at the isomiR level using joined reads, with several choices of software that can be selected by the user with standardized output.
• Results are generated for each sample, analyzed and summarized for all samples in a single expression matrix.
• This information can be processed at the miRNA or isomiR level (single sequence) but also summarizing for each isomiR variant type.
• Statistical summaries can be easily accessed using the accompanied R package XICRA.stats (https://github.com/HCGB-IGTP/XICRA.stats).

• Although the pipeline is designed to take paired-end reads, it also accepts single-end reads.

• XICRA uses cutadapt [30] for the adapter trimming analysis.

• Default trimming preset parameter settings are: to keep all reads regardless of whether the adapter is found or not, a 10% maximum adapter matching error rate (mismatches, insertions and deletions), and a 3 bp minimum overlap length.
• User must provide specific adapter sequences for the trimming analysis.
• An optional previous quality checking step can be performed for each sample using FastQC [31] before the trimming analysis.

• Results are summarized for all samples using MultiQC software [32].

• Once all reads are adapter trimmed, the tool uses fastq-join from ea-utils [33] to join the two PE reads, if provided, on the overlapping ends.

• Apart from the joined reads, this tool also generates two files with the R1 and R2 reads that cannot be joined.
• As a default the minimum overlap is set to 6 bp and the maximum allowed difference for the reads to be joined is set to 0% to retain 100% matching read pairs ensuring high quality sequencing information.

• Parameters can be modified using the different options provided.

• The XICRA pipeline can continue to process either joined PE reads or SR reads.

• Two levels of mapping are implemented. The first level profiles RNA biotypes using STAR [34] to map reads against the reference genome and featureCounts [35] to extract and quantify numbers of reads by class. The second level focuses specifically on small RNA subclasses.

• Here we describe the miRNA analysis implemented within XICRA but the modularity and versatility of the pipeline would make it quite straightforward to include other RNA biotypes analyses in detail.

• For miRNAs analysis at the isomiR resolution level, XICRA allows the user to use either miraligner [26], sRNAbench from sRNAtoolbox [27] or OPTIMIR [28].

• Each software uses different strategies and might produce different results [36].
• We have included them as they allow following standardization procedures performed by miRTOP software and adopt the miR.gff3 file format [37].
• Again, the pipeline modular implementation would allow adding additional softwares converging and adapting to miRTOP and miR.gff3 format.
• For each of the softwares mentioned above and included within the miRNA module in XICRA default parameters are used.
• Some of these parameters can be modified using the different options provided.
• As a result of this miRNA module, annotation is generated that categorizes isomiRs into classes based on their sequence modifications (including iso_5p, iso_3p, iso_add, iso_snv, iso_snv_seed, iso_snv_central_offset, iso_snv_central, iso_snv_central_suppl) following miRTOP suggested classification scheme.
• A final conversion step from individual per sample miR.gff3 files into a single expression matrix is performed.
• This file serves as input for differential expression (DE) analysis.
• Information is provided for each unique sequence and indexed names contain the miRNA, the variant type and license plate (unique identifier, UID) provided by miRTOP.
• Duplicated entries at the sequence level, produced by different modifications from the same or different miRNA are discarded.

• An additional matrix is provided containing the sequence information for each encrypted UID.

• Per sample read count matrices at the isomiR level are summarized into a single expression matrix that it serves as input for DE analysis between the comparison groups of interest.

• We have generated an additional R package (XICRA.stats) that facilitates the retrieval of these matrices and parses the information included within each unique index name provided.
• The DE analysis can be done aggregating data at the mature miRNA level (i.e. hsa-miR-501-3p), by isomiR class (i.e. hsa-miR-501-3p_iso_5p), by specific length variant cluster (i.e. hsa-miR-501-3p_iso_3p:-2) or with the sequence of the read itself as the counting data.
• This is useful since different types of modification may coexist in a single sequence, and non-templated additions and internally edited sequences can differ leading to isomiRs that can fall into different categories or be derived from different mature miRNAs.
• DE analysis is performed outside of the tool with DESeq2 package in R [38].

Types of small RNAs

• miRNA（微小RNA）：微小RNA是长度约为20-24个核苷酸的短RNA分子，它们在基因表达的后转录水平上发挥调控作用。miRNA通过结合到mRNA分子的互补序列上，可以抑制翻译过程或导致mRNA的降解。miRNA参与许多细胞过程，包括发育、分化和细胞周期控制。

• tRNA（转运RNA）：转运RNA是长约70-90个核苷酸的适配器分子，在蛋白质生物合成的翻译过程中起着关键作用。它们负责将相应的氨基酸运送到核糖体，并通过其反密码子环识别mRNA上的密码子，确保在形成的蛋白质中正确排序氨基酸。

• piRNA（Piwi相互作用RNA）：Piwi相互作用RNA是一类长度通常在24至31个核苷酸之间的小RNA分子，主要存在于动物的生殖细胞中，参与转座子沉默和基因组稳定性。它们与Piwi蛋白互作，帮助抑制转座元件的转录，保护基因组完整性。

• snRNA（小核RNA）：小核RNA是一组在真核生物细胞核中发现的小RNA分子，长度约为100-200个核苷酸。它们是剪接体的重要组成部分，剪接体是负责从前mRNA分子中移除内含子的复合体。

• snoRNA（小核仁RNA）：小核仁RNA是发现在细胞的核仁中的特化RNA分子，长度约为60-300个核苷酸。它们参与rRNA的修饰和成熟过程，特别是rRNA的化学修饰过程，如甲基化和伪尿苷化。

• circRNA（环形RNA）：环形RNA是一种具有闭合圆形结构的RNA，没有常见的5'和3'端。它们可以由外显子或内含子产生，并具有多种功能，包括作为miRNA的分子海绵、影响转录和调节基因表达。CircRNAs参与许多生物学过程，并与各种疾病有关。

在生物学研究中，进行差异表达分析通常用于比较不同样本或条件下RNA分子的表达水平变化。对于上面RNA类型：

• miRNA（微小RNA）：进行差异表达分析是有意义的。miRNAs在调控基因表达和参与多种生物过程中扮演关键角色，因此，分析其在不同条件下的表达差异有助于了解其在疾病发生、发展或其他生物学过程中的功能。

• tRNA（转运RNA）：虽然tRNAs是蛋白质合成中必不可少的，但它们在不同状态下的表达量差异通常不是研究的主要焦点。尽管如此，tRNA的改变有时可以反映细胞的代谢状态或应对压力的能力，但这并不是差异表达分析的常见应用。

• piRNA（Piwi-interacting RNA）：进行差异表达分析同样有意义，尤其是在研究生殖细胞、干细胞和癌症等领域。piRNAs与转座子的沉默和基因组稳定性维护有关，因此分析其差异表达有助于揭示它们在这些过程中的作用。

• snRNA（小核RNA）和snoRNA（小核仁RNA）：这两种RNA主要参与RNA加工和修饰，如剪接和rRNA的修饰。通常情况下，对它们进行差异表达分析不是很常见，因为它们更多地涉及基本的细胞内过程。然而，如果研究的目的是特定RNA加工途径的变化，那么它们的表达分析可能是有意义的。

• circRNA（环状RNA）：进行差异表达分析是有意义的。circRNAs与许多生理过程有关，包括作为miRNA的海绵、影响基因的转录和参与疾病的发展。因此，分析circRNAs的表达差异可以帮助理解它们在不同生物学背景下的功能和作用。

总的来说，miRNA、piRNA和circRNA的差异表达分析在许多研究领域是有意义的，因为它们在疾病和生物学过程中的角色。而tRNA、snRNA和snoRNA的差异表达分析可能不那么常见，除非研究特定的生物过程或条件下它们的特定变化。

通过将 cDNA 测序读段映射到人类基因组上，我们能否确定这些读段是否来源于 circRNA？

可以通过将 cDNA 的 reads 映射到人类基因组上来帮助确定它们是否来源于 circRNA。在 circRNA 的研究中，这种映射过程中寻找的关键特征是反向剪接事件，也就是说，寻找那些正常线性剪接顺序被反向连接的 reads。

在标准的基因表达研究中，mRNA 被逆转录成 cDNA，然后生成的测序 reads 通常会映射到基因组的连续区域以表示线性剪接事件。然而，对于 circRNA，由于它们是由反向剪接形成的，因此具有独特的“头尾相连”的结构。在测序数据中，这些反向剪接或“头尾相连”的事件会导致 reads 映射到基因组的非连续区域，即一个 exon 的末尾连接到另一个 exon 的开始，这与正常的线性剪接顺序不同。

通过检测这种非典型的、非连续的映射模式，可以推断出 reads 来自于 circRNA。需要专门的生物信息学工具和算法来识别这些特殊的映射模式，从而鉴定出 circRNAs。这些工具能够识别跨越独特的反向剪接点的 reads，帮助研究者确定哪些 reads 来自 circRNAs。

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