| Paper (and link to the paper) | year published | dataset | Alignment methods used | DGE identifying methods used | Any alignment tool suggested to work better in this paper | Any DGE identifier tool suggested to perform better in this paper | Is it supported by experimental data ? | person found the paper | Notes |
|---|---|---|---|---|---|---|---|---|---|
| Evaluation of Seven Different RNA-Seq AlignmentTools Based on Experimental Data from the ModelPlantArabidopsis thaliana | 2020 | Arabidopsis thaliana | bwa, CLC Genomics Workbench, HISAT2, kallisto, RSEM, salmon and STAR | DESeq2, CLC | STAR is better in alignment (higher tolerance for soft-clipping) but the final DGE results are similar between methods | CLC produced 50% more DE genes compare to DESeq2, can't confirm if this is better | yes but only for alignment | bwa, salmon and kallisto, using the transcriptomic reference, identified less genes. This difference is due to the presence of non-coding RNAs such as transfer RNAs (tRNA) and micro RNAs (miRNA) in the genomic reference, which are absent from the transcriptomic reference - transcripts with less than five counts were filtered | Maryam |