An analysis of the feasibility of short read sequencing

Several methods for ultra high-throughput DNA sequencing are currently under investigation. Many of these methods yield very short blocks of sequence information (reads). Here we report on an analysis showing the level of genome sequencing possible as a function of read length. It is shown that re-sequencing and de novo sequencing of the majority of a bacterial genome is possible with read lengths of 20-30 nt, and that reads of 50 nt can provide reconstructed contigs (a contiguous fragment of sequence data) of 1000 nt and greater that cover 80% of human chromosome 1

Global Landscape Structure and the Random MAX-SAT Phase Transition

We revisit the fitness landscape structure of random MAX-SAT instances, and address the question: what structural features change when we go from easy underconstrained instances to hard overconstrained ones? Some standard techniques such as autocorrelation analysis fail to explain what makes instances hard to solve for stochastic local search algorithms, indicating that deeper landscape features are required to explain the observed performance differences. We address this question by means of local optima network (LON) analysis and visualisation. Our results reveal that the number, size, and, most importantly, the connectivity pattern of local and global optima change significantly over the easy-hard transition. Our empirical results suggests that the landscape of hard MAX-SAT instances may feature sub-optimal funnels, that is, clusters of sub-optimal solutions where stochastic local search methods can get trapped

Assessment of disease progression in dysferlinopathy: A 1-year cohort study

ObjectiveTo assess the ability of functional measures to detect disease progression in dysferlinopathy over 6 months and 1 year.MethodsOne hundred ninety-three patients with dysferlinopathy were recruited to the Jain Foundation's International Clinical Outcome Study for Dysferlinopathy. Baseline, 6-month, and 1-year assessments included adapted North Star Ambulatory Assessment (a-NSAA), Motor Function Measure (MFM-20), timed function tests, 6-minute walk test (6MWT), Brooke scale, Jebsen test, manual muscle testing, and hand-held dynamometry. Patients also completed the ACTIVLIM questionnaire. Change in each measure over 6 months and 1 year was calculated and compared between disease severity (ambulant [mild, moderate, or severe based on a-NSAA score] or nonambulant [unable to complete a 10-meter walk]) and clinical diagnosis.ResultsThe functional a-NSAA test was the most sensitive to deterioration for ambulant patients overall. The a-NSAA score was the most sensitive test in the mild and moderate groups, while the 6MWT was most sensitive in the severe group. The 10-meter walk test was the only test showing significant change across all ambulant severity groups. In nonambulant patients, the MFM domain 3, wrist flexion strength, and pinch grip were most sensitive. Progression rates did not differ by clinical diagnosis. Power calculations determined that 46 moderately affected patients are required to determine clinical effectiveness for a hypothetical 1-year clinical trial based on the a-NSAA as a clinical endpoint.ConclusionCertain functional outcome measures can detect changes over 6 months and 1 year in dysferlinopathy and potentially be useful in monitoring progression in clinical trials.ClinicalTrials.gov identifier:NCT01676077

Experimental Rugged Fitness Landscape in Protein Sequence Space

The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12–130 of the initial random polypeptide and selection for infectivity, the selected phage showed a 1.7×10(4)-fold increase in infectivity, defined as the number of infected cells per ml of phage suspension. Fitness was defined as the logarithm of infectivity, and we analyzed (1) the dependence of stationary fitness on library size, which increased gradually, and (2) the time course of changes in fitness in transitional phases, based on an original theory regarding the evolutionary dynamics in Kauffman's n-k fitness landscape model. In the landscape model, single mutations at single sites among n sites affect the contribution of k other sites to fitness. Based on the results of these analyses, k was estimated to be 18–24. According to the estimated parameters, the landscape was plotted as a smooth surface up to a relative fitness of 0.4 of the global peak, whereas the landscape had a highly rugged surface with many local peaks above this relative fitness value. Based on the landscapes of these two different surfaces, it appears possible for adaptive walks with only random substitutions to climb with relative ease up to the middle region of the fitness landscape from any primordial or random sequence, whereas an enormous range of sequence diversity is required to climb further up the rugged surface above the middle region

Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis

Motivation : Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising the structure of HMMs would be highly desirable. However, this raises two important issues; firstly, the new HMMs should be biologically interpretable, and secondly we need to control the complexity of the HMM so that it has good generalisation performance on unseen sequences. In this paper we explore the possibility of using a Genetic Algorithm (GA) for optimising the HMM structure. GAs are sufficiently flexible to allow incorporation of other techniques such as Baum-Welch training within their evolutionary cycle. Furthermore, operators which alter the structure of HMMs can be designed to favour interpretable and simple structures. Results : In this paper a training strategy using Genetic Algorithms is proposed, and it is tested on finding HMM structures for the promoter and coding region of the bacterium C. jejuni. The proposed Genetic Algorithm for Hidden Markov Models (GA-HMM) allows HMMs with different numbers of states to evolve. To prevent over-fitting a separate data set is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The GA-HMM was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has previously been published

Analysis of Synfire Chains

The biological implications of synfire chain neural networks are explored by studying two idealized models. In the first a network model is proposed with binary firing neurons and parallel updating. This model can be solved exactly in the thermodynamic limit using mean field theory. An explicit equation for the capacity is obtained. In the second model the synchrony of the pulse of activity along a synfire chain is investigated in the context of simple integrate-and-fire neurons. It is found that under natural assumptions a near synchronous wave of activity can stably propagate along a synfire chain. The relevance of this result to real systems is discussed. 1 Introduction Synfire chains have been proposed by Abeles [1, 2] as a model of cortical function. Interest in them has grown because they provide an explanation for otherwise mysterious measurements of precise spike timing. A synfire chain consists of small pools of neurons linked together in a feedforward chain so that a wave of..