Comparative genomics beyond sequence-based alignments: RNA structures in the ENCODE regions

Abstract

Recent computational scans for non-coding RNAs (ncRNAs) in multiple organisms have relied on existing multiple sequence alignments. However, as sequence similarity drops, a key signal of RNA structure--frequent compensating base changes--is increasingly likely to cause sequence-based alignment methods to misalign, or even refuse to align, homologous ncRNAs, consequently obscuring that structural signal. We have used CMfinder, a structure-oriented local alignment tool, to search the ENCODE regions of vertebrate multiple alignments. In agreement with other studies, we find a large number of potential RNA structures in the ENCODE regions. We report 6587 candidate regions with an estimated false-positive rate of 50%. More intriguingly, many of these candidates may be better represented by alignments taking the RNA secondary structure into account than those based on primary sequence alone, often quite dramatically. For example, approximately one-quarter of our predicted motifs show revisions in >50% of their aligned positions. Furthermore, our results are strongly complementary to those discovered by sequence-alignment-based approaches--84% of our candidates are not covered by Washietl et al., increasing the number of ncRNA candidates in the ENCODE region by 32%. In a group of 11 ncRNA candidates that were tested by RT-PCR, 10 were confirmed to be present as RNA transcripts in human tissue, and most show evidence of significant differential expression across tissues. Our results broadly suggest caution in any analysis relying on multiple sequence alignments in less well-conserved regions, clearly support growing appreciation for the biological significance of ncRNAs, and strongly support the argument for considering RNA structure directly in any searches for these elements.

Figure 1.

Score distribution of the full CMfinder input set (A) composite score and (B) consensus minimum free energies for the native and random (shuffled) sequences. There is a slight shift toward lower energy and higher score for our native data.

Figure 2.

Overlap of predictions made by CMfinder, RNAz, and EvoFold. Only predictions that are not highly conserved (phastCons), outside exons, and repeat regions are considered, since these regions are the common subset of the input regions to these three programs. The total number for each program is indicated in parentheses below the label.

Figure 3.

Average pairwise sequence similarity of the predicted motifs versus the fraction that has been realigned compared to the original alignments.

Figure 4.

Expression of predicted ncRNA candidates by RT-PCR and Northern blot analysis. (A) Strand-specific RT-PCR analysis of ncRNA candidates on human RNA pools (see Methods). β-Actin was used as control, yielding PCR products in the presence of reverse transcriptase (RT+), but not in its absence (RT−). (B) Tissue-specific expression of ncRNA candidates as evaluated by RT-PCR analysis of human RNA samples. The same β-actin controls as for A were used. (C) Expression of ncRNA candidates within the human CNS as evaluated by RT-PCR analysis. The same β-actin controls as for A and B were used. (D) Expression of ncRNA candidate #6 as evaluated by Northern blotting of human RNA samples from 11 tissues.