maxAlike: maximum likelihood-based sequence reconstruction with application to improved primer design for unknown sequences

Abstract

Motivation: The task of reconstructing a genomic sequence from a particular species is gaining more and more importance in the light of the rapid development of high-throughput sequencing technologies and their limitations. Applications include not only compensation for missing data in unsequenced genomic regions and the design of oligonucleotide primers for target genes in species with lacking sequence information but also the preparation of customized queries for homology searches.

Results: We introduce the maxAlike algorithm, which reconstructs a genomic sequence for a specific taxon based on sequence homologs in other species. The input is a multiple sequence alignment and a phylogenetic tree that also contains the target species. For this target species, the algorithm computes nucleotide probabilities at each sequence position. Consensus sequences are then reconstructed based on a certain confidence level. For 37 out of 44 target species in a test dataset, we obtain a significant increase of the reconstruction accuracy compared to both the consensus sequence from the alignment and the sequence of the nearest phylogenetic neighbor. When considering only nucleotides above a confidence limit, maxAlike is significantly better (up to 10%) in all 44 species. The improved sequence reconstruction also leads to an increase of the quality of PCR primer design for yet unsequenced genes: the differences between the expected T(m) and real T(m) of the primer-template duplex can be reduced by ~26% compared with other reconstruction approaches. We also show that the prediction accuracy is robust to common distortions of the input trees. The prediction accuracy drops by only 1% on average across all species for 77% of trees derived from random genomic loci in a test dataset.

Availability: maxAlike is available for download and web server at: http://rth.dk/resources/maxAlike.

Fig. 1.

The steps of the maxAlike algorithm. From the input, consisting of a multiple alignment and a phylogenetic tree, the algorithm computes PSSMs and reconstructed sequences for the target species. The output can readily be applied to primer design and homology search.

Fig. 2.

Dataset MZ44-2: median MATCH scores for maxAlike (ML) and nucleotide frequency (Freq) PSSMs for each species compared with the average distance to its phylogenetically closest neighbor.

Fig. 3.

Dataset MZ44-1: recovery rates in percent for sequences reconstructed by maxAlike (ML), frequency-based consensus (Freq) and nearest neighbor (NN). Each point is one species plotted as its average distance to the phylogenetically nearest neighbor. (a) threshold 0.5. (b) no threshold.

Fig. 4.

Dataset MZ44-1: (a) Average change of total recovery rates across all species for different sets of input trees: gene tree (F); reference species tree (S); (1–10) bins with trees estimated from other genomic loci; increasing bin number corresponds to higher topological distance to reference tree. (b) Change in the T_m difference due to increased number of mismatches in the primer sequence.

Fig. 5.

Average change of total recovery rates across all species for different sets of input trees: gene tree (F); reference species tree (S); bins with trees having distorted branch lengths using the specified relative normal errors. (a) MZ44-1. (b) MZ44-2.

Fig. 6.

Dataset MZ44-2: average differences of the expected and actual melting temperature T_m of the primer–template duplex for primers derived from maxAlike (threshold 0.5) and Freq (threshold 0.5) reconstructed sequences and nearest neighbor (NN) sequence for each species, sorted by average distance to its phylogenetically nearest neighbor.