s to facilitate bioinformatics analysis of large datasets and using different HTS platforms, such as OMICTools \[[@B37-viruses-10-00528]\], CLCBio \[[@B38-viruses-10-00528]\], Genedata Selector \[[@B39-viruses-10-00528]\], Archetype Software \[[@B40-viruses-10-00528]\], and CosmosID \[[@B41-viruses-10-00528]\].

Some potential pathways for bioinformatics analysis pipelines are illustrated in [Figure 2](#viruses-10-00528-f002){ref-type="fig"}. The details of each process are discussed in the sections below. Within each element of the analysis pipeline, specific choices can affect the detection of rare or novel viral sequences, and the computational efforts involved \[[@B42-viruses-10-00528]\]. For instance, the total number of short reads or assembled contigs, and the size of databases used for host sequence removal (if used) or data analysis, will directly influence the computational time and computer resources used. Analysis of all-frame-translated protein sequences may afford a greater chance of detecting novel viral sequences that are not represented in the nucleotide database. Developers of pipelines must make choices that afford adequate sensitivity for detection of viruses at acceptable computational cost. It is prudent to save any unmapped reads for future analysis as databases are updated with new virus sequences.

In the rest of this paper, we discuss some of the specific elements of the bioinformatics analysis pipelines, and offer our current thinking on their strengths and limitations.

4.1. Sequence Read Pre-Processing, Assembly, and Alignment {#sec4dot1-viruses-10-00528}
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Sequencing reads may suffer from quality issues, such as low-scoring base calls or the presence of platform-specific adapter sequences. A variety of tools are available to evaluate the existence of such issues, such as FastQC \[[@B43-viruses-10-00528]\] or MEEPTOOLS \[[@B44-viruses-10-00528]\], which generates a number of useful graphical plots, for example, displaying quali