Uncertainty in homology inferences: Assessing and improving genomic sequence alignment

Abstract

Sequence alignment underpins all of comparative genomics, yet it remains an incompletely solved problem. In particular, the statistical uncertainty within inferred alignments is often disregarded, while parametric or phylogenetic inferences are considered meaningless without confidence estimates. Here, we report on a theoretical and simulation study of pairwise alignments of genomic DNA at human-mouse divergence. We find that >15% of aligned bases are incorrect in existing whole-genome alignments, and we identify three types of alignment error, each leading to systematic biases in all algorithms considered. Careful modeling of the evolutionary process improves alignment quality; however, these improvements are modest compared with the remaining alignment errors, even with exact knowledge of the evolutionary model, emphasizing the need for statistical approaches to account for uncertainty. We develop a new algorithm, Marginalized Posterior Decoding (MPD), which explicitly accounts for uncertainties, is less biased and more accurate than other algorithms we consider, and reduces the proportion of misaligned bases by a third compared with the best existing algorithm. To our knowledge, this is the first nonheuristic algorithm for DNA sequence alignment to show robust improvements over the classic Needleman-Wunsch algorithm. Despite this, considerable uncertainty remains even in the improved alignments. We conclude that a probabilistic treatment is essential, both to improve alignment quality and to quantify the remaining uncertainty. This is becoming increasingly relevant with the growing appreciation of the importance of noncoding DNA, whose study relies heavily on alignments. Alignment errors are inevitable, and should be considered when drawing conclusions from alignments. Software and alignments to assist researchers in doing this are provided at

Detection of nonneutral substitution rates on mammalian phylogenies

“…However, at present, these methods require orders of magnitude more computational time than methods that assumed fixed alignments and are not feasible for use on a genome-wide scale. Still, it may be possible to use heuristic methods to substantially improve the speed of such methods (Bradley et al 2009;Paten et al 2009), or to quantify alignment uncertainty and then use this information in downstream functional element identification (Lunter et al 2008). In short, many opportunities remain for improving the biological realism, statistical power, and robustness of methods for identifying functional elements from comparative sequence data.…”

Primer design and in silico analysis using CLUSTALW and MUSCLE for L-arabinose isomerase (araA) gene detection in thermophilic bacteria

“…The gap can be formed as a manifestation of a high occurrence of indels (insertion and deletion). The occurrence of indels in bacteria can be caused by mutation, genetic recombination and transformation [22].…”

Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

“…Based on these Forward calculations, we run the Backward algorithm and calculate posterior probabilities for every pair of residues in each of the three pairwise alignments per triplet. Finally, we use marginalized posterior decoding approach, as described in Lunter et al (2008), to identify high confidence triplets of homologous sites across all pairs, which are then used to estimate the substitution model using the standard Felsenstein tree likelihood (Felsenstein 1981). To estimate the indel parameters, and ε, we only use the set of pairwise alignments from our triplets and maximize the likelihood of state transitions given the previously estimated divergence times on the triplets.…”