Optimum allocation of test resources and comparison of breeding strategies for hybrid wheat

Key message

The use of a breeding strategy combining the evaluation of line per se with testcross performance maximizes annual selection gain for hybrid wheat breeding.

Abstract

Recent experimental studies confirmed a high commercial potential for hybrid wheat requiring the design of optimum breeding strategies. Our objectives were to (1) determine the optimum allocation of the type and number of testers, the number of test locations and the number of doubled haploid lines for different breeding strategies, (2) identify the best breeding strategy and (3) elaborate key parameters for an efficient hybrid wheat breeding program. We performed model calculations using the selection gain for grain yield as target variable to optimize the number of lines, testers and test locations in four different breeding strategies. A breeding strategy (BS2) combining the evaluation of line per se performance and general combining ability (GCA) had a far larger annual selection gain across all considered scenarios than a breeding strategy (BS1) focusing only on GCA. In the combined strategy, the production of testcross seed conducted in parallel with the first yield trial for line per se performance (BS2_rapid) resulted in a further increase of the annual selection gain. For the current situation in hybrid wheat, this relative superiority of the strategy BS2_rapid amounted to 67 % in annual selection gain compared to BS1. Varying a large number of parameters, we identified the high costs for hybrid seed production and the low variance of GCA in hybrid wheat breeding as key parameters limiting selection gain in BS2_rapid.     

Comparative study of sequence aligners for detecting antibiotic resistance in bacterial metagenomes

We aim to compare the performance of Bowtie2 , bwa‐mem , blastn and blastx when aligning bacterial metagenomes against the Comprehensive Antibiotic Resistance Database (CARD). Simulated reads were used to evaluate the performance of each aligner under the following four performance criteria: correctly mapped, false positives, multi‐reads and partials. The optimal alignment approach was applied to samples from two wastewater treatment plants to detect antibiotic resistance genes using next generation sequencing. blastn mapped with greater accuracy among the four sequence alignment approaches considered followed by Bowtie2 . blastx generated the greatest number of false positives and multi‐reads when aligned against the CARD. The performance of each alignment tool was also investigated using error‐free reads. Although each aligner mapped a greater number of error‐free reads as compared to Illumina‐error reads, in general, the introduction of sequencing errors had little effect on alignment results when aligning against the CARD. Given each performance criteria, blastn was found to be the most favourable alignment tool and was therefore used to assess resistance genes in sewage samples. Beta‐lactam and aminoglycoside were found to be the most abundant classes of antibiotic resistance genes in each sample.

Significance and Impact of the Study

Antibiotic resistance genes (ARGs) are pollutants known to persist in wastewater treatment plants among other environments, thus methods for detecting these genes have become increasingly relevant. Next generation sequencing has brought about a host of sequence alignment tools that provide a comprehensive look into antimicrobial resistance in environmental samples. However, standardizing practices in ARG metagenomic studies is challenging since results produced from alignment tools can vary significantly. Our study provides sequence alignment results of synthetic, and authentic bacterial metagenomes mapped against an ARG database using multiple alignment tools, and the best practice for detecting ARGs in environmental samples.     

A new degree of freedom for memory allocation in clusters

Improvements in parallel computing hardware usually involve increments in the number of available resources for a given application such as the number of computing cores and the amount of memory. In the case of shared-memory computers, the increase in computing resources and available memory is usually constrained by the coherency protocol, whose overhead rises with system size, limiting the scalability of the final system. In this paper we propose an efficient and cost-effective way to increase the memory available for a given application by leveraging free memory in other computers in the cluster. Our proposal is based on the observation that many applications benefit from having more memory resources but do not require more computing cores, thus reducing the requirements for cache coherency and allowing a simpler implementation and better scalability. Simulation results show that, when additional mechanisms intended to hide remote memory latency are used, execution time of applications that use our proposal is similar to the time required to execute them in a computer populated with enough local memory, thus validating the feasibility of our proposal. We are currently building a prototype that implements our ideas. The first results from real executions in this prototype demonstrate not only that our proposal works but also that it can efficiently execute applications that make use of remote memory resources.     

Neuroevolution and complexifying genetic architectures for memory and control tasks

The way genes are interpreted biases an artificial evolutionary system towards some phenotypes. When evolving artificial neural networks, methods using direct encoding have genes representing neurons and synapses, while methods employing artificial ontogeny interpret genomes as recipes for the construction of phenotypes. Here, a neuroevolution system (neuroevolution with ontogeny or NEON) is presented that can emulate a well-known neuroevolution method using direct encoding (neuroevolution of augmenting topologies or NEAT), and therefore, can solve the same kinds of tasks. Performance on challenging control and memory benchmark tasks is reported. However, the encoding used by NEON is indirect, and it is shown how characteristics of artificial ontogeny can be introduced incrementally in different phases of evolutionary search.     

A platform for biological sequence comparison on parallel computers

We have written two programs for searching biological sequencedatabases that run on Intel hypercube computers. PSCANLJB comparesa single sequence against a sequence library, and PCOMPLIB comparesall the entries in one sequence library against a second library.The programs provide a general framework for similarity searching;they include functions for reading in query sequences, searchparameters and library entries, and reporting the results ofa search. We have isolated the code for the specific functionthat calculates the similarity score between the query and librarysequence; alternative searching algorithms can be implementedby editing two files. We have implemented the rapid FASTA sequencecomparison algorithm and the more rigorous Smith — Watermanalgorithm within this framework. The PSCANLIB program on a 16node iPSC/2 80386-based hypercube can compare a 229 amino acidprotein sequence with a 3.4 million residue sequence libraryin {small tilde}16s with the FASTA algorithm. Using the Smith— Waterman algorithm, the same search takes 35 min. ThePCOMPUB program can compare a 0.8 millon amino acid proteinsequence library with itself in 5.3 min with FASTA on a third-generation32 node Intel iPSC/860 hypercube. Received on September 8, 1990; accepted on December 15, 1990     

A probabilistic measure for alignment-free sequence comparison

MOTIVATION: Alignment-free sequence comparison methods are still in the early stages of development compared to those of alignment-based sequence analysis. In this paper, we introduce a probabilistic measure of similarity between two biological sequences without alignment. The method is based on the concept of comparing the similarity/dissimilarity between two constructed Markov models. RESULTS: The method was tested against six DNA sequences, which are the thrA, thrB and thrC genes of the threonine operons from Escherichia coli K-12 and from Shigella flexneri; and one random sequence having the same base composition as thrA from E.coli. These results were compared with those obtained from CLUSTAL W algorithm (alignment-based) and the chaos game representation (alignment-free). The method was further tested against a more complex set of 40 DNA sequences and compared with other existing sequence similarity measures (alignment-free). AVAILABILITY: All datasets and computer codes written in MATLAB are available upon request from the first author.     

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.     

Computer comparison of new and existing criteria for constructing evolutionary trees from sequence data

Summary Three new methods for constructing evolutionary trees from molecular sequence data are presented. These methods are based on a theory for correcting for non-constant evolutionary rates (Klotz et al. 1979; Klotz and Blanken 1981). Extensive computer simulations were run to compare these new methods to the commonly used criteria of Dayhoff (1978) and Fitch and Margoliash (1967). The results of these simulations showed that two of the new methods performed as well as Dayhoff's criterion, significantly better than that of Fitch and Margoliash, and as well as a simple variation of the latter (Prager and Wilson 1978) where any topology containing negative branch mutations is discarded. However, no method yielded the correct topology all of the time, which demonstrated the need to determine confidence estimates in a particular result when evolutionary trees are determined from sequence data.     

A novel Markov pairwise protein sequence alignment method for sequence comparison

The Smith-Waterman (SW) algorithm is a typical technique for local sequence alignment in computational biology. However, the SW algorithm does not consider the local behaviours of the amino acids, which may result in loss of some useful information. Inspired by the success of Markov Edit Distance (MED) method, this paper therefore proposes a novel Markov pairwise protein sequence alignment (MPPSA) method that takes the local context dependencies into consideration. The numerical results have shown its superiority to the SW for pairwise protein sequence comparison.     

A simple method for global sequence comparison.

A simple method of sequence comparison, based on a correlation analysis of oligonucleotide frequency distributions, is here shown to be a reliable test of overall sequence similarity. The method does not involve sequence alignment procedures and permits the rapid screening of large amounts of sequence data. It identifies those sequences which deserve more careful analysis of sequence similarity at the level of resolution of the single nucleotide. It uses observed quantities only and does not involve the adoption of any theoretical model.     

A comparison of membranes and enrichment strategies for microbial fuel cells

The external resistance is perhaps the easiest way to influence the operation of a microbial fuel cell (MFC). In this paper, three enrichment strategies, whereby the external resistance was fixed at: (1) a high value in order to maximize the cell voltage (U strategy); (2) a low value in order to maximize the current (I strategy); and (3) a value equal to the internal resistance of the MFC to maximize the power output (P strategy), were investigated. The I strategy resulted in increased maximum power generation and the likely reason is that electron transfer was facilitated under low external resistance, which in turn, favored the development of an electrochemically active biofilm. This experiment was conducted in a single-chamber MFC system equipped with a membrane electrode assembly, and a comparison of the performance achieved by five different membranes is also provided. Selemion was found to be a suitable alternative to Nafion.     

Asymmetrically reliable caches for multicore architectures under performance and energy constraints

Cache structures in a multicore system are more vulnerable to soft errors due to high transistor density. Protecting all caches unselectively has notable overhead on performance and energy consumption. In this study, we propose asymmetrically reliable caches to supply reliability need of the system using sufficient additional hardware under the performance and energy constraints. In our framework, a chip multiprocessor is composed of a high reliability core which has ECC protection, and a set of low reliability cores which have no protection on their data caches. Between two types of cores, there is also a middle-level reliability core which has only parity check. Application threads are mapped on the different cores in terms of reliability based on their critical data usage. The experimental results for selected applications show that our proposed techniques improve reliability with considerable performance and energy overhead on the average compared to traditional unsafe caches.     

A comparison of scoring functions for protein sequence profile alignment

MOTIVATION: In recent years, several methods have been proposed for aligning two protein sequence profiles, with reported improvements in alignment accuracy and homolog discrimination versus sequence-sequence methods (e.g. BLAST) and profile-sequence methods (e.g. PSI-BLAST). Profile-profile alignment is also the iterated step in progressive multiple sequence alignment algorithms such as CLUSTALW. However, little is known about the relative performance of different profile-profile scoring functions. In this work, we evaluate the alignment accuracy of 23 different profile-profile scoring functions by comparing alignments of 488 pairs of sequences with identity < or =30% against structural alignments. We optimize parameters for all scoring functions on the same training set and use profiles of alignments from both PSI-BLAST and SAM-T99. Structural alignments are constructed from a consensus between the FSSP database and CE structural aligner. We compare the results with sequence-sequence and sequence-profile methods, including BLAST and PSI-BLAST. RESULTS: We find that profile-profile alignment gives an average improvement over our test set of typically 2-3% over profile-sequence alignment and approximately 40% over sequence-sequence alignment. No statistically significant difference is seen in the relative performance of most of the scoring functions tested. Significantly better results are obtained with profiles constructed from SAM-T99 alignments than from PSI-BLAST alignments. AVAILABILITY: Source code, reference alignments and more detailed results are freely available at http://phylogenomics.berkeley.edu/profilealignment/     

A comparison of alternate metabolic strategies for the utilization of D-arabinose

Summary Mutants ofKlebsiella aerogenes W70 that metabolize the uncommon pentose D-arabinose were isolated. These mutants were found to be either constitutive or indicible by D-arabinose for the synthesis of enzymes in the L-fucose pathway. Such mutants could then utilize L-fucose isomerase to convert the structurally similar D-arabinose molecule to D-ribulose. D-Ribulose is an inter-mediate and the inducer of an existing ribitol pathway and could thus be metabolized. In those D-arabinose-positive mutants where the ribitol pathway was blocked by mutation, D-ribulose could alternatively be metabolized by using the remaining L-fucose pathway enzymes. When the two D-arabinose catabolic routes were compared, catabolism of D-arabinose via the ribitol pathway was found to be more efficient. Catabolism of D-arabinose using the L-fucose pathway per-mitted D-ribulose to escape into the media and produced an unmetabolizable end product, L-glycolic acid. A comparison of growth using constitutive versus inducible control of the borrowed L-fucose isomerase did not reveal an advantage for one control type over the other. Several differences were observed,however, when we determined the degree to which these control mutations perturbed the normal functioning of the L-fucose and associated pathways. Growth of the constitutive mutant was impaired with L-fucose as substrate. The inducible-control mutant had altered growth characteristics on ribitol and L-rhamnose.     

A comparison of different strategies for lipase-catalyzed synthesis of partial glycerides

Summary Four different approaches for the synthesis of monolaurylglycerol (MLG) by non specificPseudomonas cepacia lipase in a crude and purified form have been studied: a. The direct esterification of glycerol by lauric acid in bis-(2-ethylhexyl)sulfosuccinate sodium salt (AOT)/isooctane microemulsion systems; b. the transesterification of glycerol by vinyl laurate in the presence or not of any solvent; c. solid-phase glycerolysis of trilaurin; and, d. transesterification of protected glycerol, 1,2-O-isopropylidene glycerol, by vinyl laurate, in the presence or not of any solvent. It was found that in the two latter cases (d and c) the formation of pure MLG was occurred, while in the first two cases (a and b) apart of MLG the formation of DLG was also observed.     

A likelihood ratio test for species membership based on DNA sequence data

DNA barcoding as an approach for species identification is rapidly increasing in popularity. However, it remains unclear which statistical procedures should accompany the technique to provide a measure of uncertainty. Here we describe a likelihood ratio test which can be used to test if a sampled sequence is a member of an a priori specified species. We investigate the performance of the test using coalescence simulations, as well as using the real data from butterflies and frogs representing two kinds of challenge for DNA barcoding: extremely low and extremely high levels of sequence variability.     

Deep learning architectures for prediction of nucleosome positioning from sequences data

Background

Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation.

Results

In this work, we propose a deep learning model for nucleosome identification. Our model stacks convolutional layers and Long Short-term Memories to automatically extract features from short- and long-range dependencies in a sequence. Using this model we are able to avoid the feature extraction and selection steps while improving the classification performances.

Conclusions

Results computed on eleven data sets of five different organisms, from Yeast to Human, show the superiority of the proposed method with respect to the state of the art recently presented in the literature.