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Due to the high price of the commercial version of the program, Autodesk also offers a free student version, which explicitly states that it is to be used for "educational purposes only". The student version has identical features to the full version, but is only for single use and cannot be installed on a network. The student license expires after three years, at which time the user, if they are still a student, may download the latest version, thus renewing the license for another three years.
Pre-trained models interrogate specific sites and classify the data as supporting a modified or unmodified base. Nanopolish [66] detects 5mC with a hidden Markov model, which in signalAlign [107] is combined with a hierarchical Dirichlet process, to determine the most likely k-mer (modified or unmodified). D-NAscent [95] utilises an approach similar to Nanopolish to detect BrdU incorporation, while EpiNano uses support vector machines (SVMs) to detect RNA m6A. Recent methods use neural network classifiers to detect 6mA and 5mC (mCaller [108], DeepSignal [110], DeepMod [111]). The accuracy of these methods is upwards of 80% but varies between modifications and motifs. Appropriate training data is crucial and currently a limiting factor. Models trained exclusively on samples with fully methylated or unmethylated CpGs will not perform optimally on biological samples with a mixture of CpG and mCpGs, or 5mC in other sequence contexts [66, 105]. Low specificity is particularly problematic for low abundance marks. m6A is present at 0.05% in mRNA [113, 114]; therefore, a method testing all adenosines in the transcriptome with sensitivity and specificity of 90% at the single-molecule, single-base level would result in an unacceptable false discovery rate of 98%.
Types of transcriptomic analyses and their steps. The choice of sequencing protocol amongst the six available workflows affects the type, characteristics, and quantity of data generated. Only direct RNA sequencing allows epitranscriptomic studies, but SMRT direct RNA sequencing is a custom technique that is not fully supported. The remaining non-exclusive applications are isoform detection, quantification, and differential analysis. The dashed lines in arrows represent upstream processes to transcriptomics
Alternative isoform detection pipelines such as IsoCon [130], SQANTI [131], and TALON [132] attempt to mitigate the erroneous merging of similar transcripts of the Iso-Seq pipeline. IsoCon and SQANTI specifically work with SMRT data while TALON is a technology-independent approach. IsoCon uses the full-length transcripts from Iso-Seq to perform clustering and partial error correction and identify candidate transcripts without losing potential true variants within each cluster. SQANTI generates quality control reports for SMRT Iso-Seq data and detects and removes potential artefacts. TALON, on the other hand, relies heavily on the GENCODE annotation. Since both IsoCon and TALON focus on the human genome, they may not perform equally well with genomes from non-model organisms. A number of alternative isoform annotation pipelines for SMRT and/or nanopore data have recently emerged, such as FLAIR [133], Tama [134], IDP [122], TAPIS [135], Mandalorion Episode II [36, 57], and Pinfish [136]. Some of them use short reads to improve exon junction annotation. However, their accuracy has not yet been extensively tested.
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