Workflow to identify coding and long non-coding RNAs (lncRNAs) from RNA-Seq libraries

This is a workflow mainly involved in the analysis of small RNA-seq Dual libraries, coming from non-model organism, with just one replicate, low depth and genome independent. However, it's very flexible and you can take a look of the general purpose of this workflow, its scripts and contribute as well!

• Workflow overview:
  

  • Step 1: Preprocessing of input data
  
  
  • Step 2: de novo assembly of transcripts
  
  
  • Step 3: Transcriptome quality assessment
  
  
  • Step 4: Merging assemblies
  
  
  • Step 5: Coding potential
  
  
  • Step 6: Annotation

• Contact
  

• References
  

• Keynotes

• Acknowledgements

Pipeline overview

STEP 1: Preprocessing of input data

It consists in the removal of low quality information, artifacts such as adaptadors, and contamination, with Trimmomatic (Bolger et al. 2014). As this pipeline is mainly related to input data coming from RNA-seq Dual analysis, it's important to split the reads in order to get those from the host and the pathogen, separately. BBDuk (Bushnell 2018) performs very well this task by a k-mer based approach. It's also recommended to align those separated reads against several rRNA databases with SortMeRNA (Kopylova et al. 2012), in order to avoid alignment and differential expression noise. At last, if there's available a reference genome, it will be very useful for downstream analysis, if those separated and rRNA depleted reads are mapped against that genome, to assure clean reads coming only from either the pathogen or host.

STEP 2: de novo assembly of transcripts

Different strategies of de novo assembly of transcripts are needed in order to appreciate the performance of various tools, even though they rely in similar principles such as de Bruijn graphs (Wang y Gribskov 2017). In this step, several de novo assemblers such as the well-known Trinity (Grabherr et al. 2011), its genome-guided assembly with HISAT2 (Kim et al. 2015) and STAR (Dobin et al. 2013), rnaSPAdes (Bushmanova et al. 2019), IDBA-tran (Peng et al. 2013), and Transabyss (Robertson et al. 2010) are implemented on the clean reads with means to get several assemblies to be evaluated next.

STEP 3: Transcriptome quality assessment

This step accounts for the quality analysis of each transcriptome by free-alignment software Transrate (Smith-Unna et al. 2016) and alignment-dependent software like BUSCO (Simão et al. 2015) and rnaQuast (Bushmanova et al. 2016). As there are no global parameters to rely quality on, metrics such as complete BUSCOs, contigs coverage, assembly transrate score and transcript length are assessed (Hölzer y Marz). Those assemblies with highest scores of each metric are next merged into a single assembly.

STEP 4: Merging assemblies

To merge the best assemblies, the script concatenator.pl (Cerveau y Jackson 2016) and the software CD-HIT-EST (Fu et al. 2012) are implemented. They receive the transcriptome or assemblies to be merged, and their output consists of one single heteregeneuos asseembly made of unique and non-redundant transcripts. It's been seen that this approach can raise the quality of the overall transcriptome as well as retain some unique transcripts generated by the different assembly tools (Cerveau y Jackson 2016).

STEP 5: Coding potential

The coding potential of a sequence can be defined as its capacity to code for protein (Wucher et al. 2017). There are several tools that try to assess this feature by distinct approaches. Some rely on sequence intrinsic features, PLEK (Li et al. 2015), and others on protein-alignments such as CPC2 (Kang et al. 2017) and PORTRAIT (Arrial et al. 2009). In spite of the low or none coding potential of long non-coding RNAs (lncRNAs), they have a very similar structure regarding mRNAS (Huarte 2013). That's why instead of choosing one coding potential analysis tool, we aim to find the common coding and non-coding transcripts among various tools (PLEK, PORTRAIT, CPC2) by designing a notebook (https://biocdgv.github.io/Piperaction/Noncoding_finder.html#raw). This approach has also been tested in Kumar et al. 2019. Last but not least, in order to assure the transcripts's coding potential, another filter is made by FEELnc (Wucher et al. 2017), to analyze once again the coding nature of the non-coding transcripts and identify some features of the lncRNAs found, like their direction, type, subtype and their neighboring coding genes.

STEP 6: Annotation

After dissecting the merged transcriptome into their coding potential counterparts, one of the goals of this pipeline comes: the annotation. It consists in finding functions of each of the given sequences. First, for the lncRNAs identified, rfam-scan.pl (Kalvari et al. 2018) from the family of non-coding RNA databases (RFAM) is used. Further with the coding transcripts and the neihboring coding genes (PRNAs), Blast (Johnson et al. 2008), PFAM (Bateman et al. 2004) and the database of pathogen and host interaction (Phi-db)(Winnenburg et al. 2006) are implemented, in order to find only those transcripts related with pathogenesis itself. After having those transcripts well functionally analyzed, their biological processes are also investigated by Gene ONTOLOGY (GO) analysis (GO consortium 2004) and InterProScan (Quevillon et al. 2005). In addition, the presence of signal peptides within their sequences by SignalP5.0 (Armenteros et al. 2019) is also targeted. Finally, with those candidate pathogenicity genes, some potential protein interactions were proposed with STRING (Szklarczyk et al. 2014), by using some ID conversion with Uniprot (Uniprot consortium 2007).

Annotation tools's links:

pfam-scan

rfam-scan

blastn

Phi-blast

GO

SignalP5.0

Uniprot

STRING

CONTACT:

if you want to contribute to make this pipeline better and optimizable, please contact me at: biocdgv@gmail.com.

Keynotes:

At this very moment, this pipeline is not automatized yet, but if you want to use any of the command lines shown, suit yourself!

Notebook where some design ideas were borrowed: https://github.com/CompSynBioLab-KoreaUniv/FunGAP

Acknowledgements:

At first, I want to thank both of the Institutions that helped me throughout my academic and professional growth, University of Antioquia and EAFIT University. Secondly to my advisors and colleagues for their practical contributions to this work. Finally, to my family who always supported me.

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