Reconstruction performance for the DREAM3 and DREAM4 in the size 100 subchallenges.

<p>Reconstruction performance for the DREAM3 and DREAM4 in the size 100 subchallenges.</p

A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data

<div><p>Motivation</p><p>Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations <i>y</i> = Φ<i>x</i>. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.</p><p>Results</p><p>In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the <i>in silico</i> networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.</p></div

Comparison of the boundary of success phase at several values of indeterminacy <i>δ</i>.

<p>Comparison of the boundary of success phase at several values of indeterminacy <i>δ</i>.</p

Comparison of the ROC and PR curves in the DREAM3 identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.

<p>(a) ROC curves of Yeast2. (b) PR curves of Yeast2.</p

Reconstruction performance of the StOMP and SmOMP algorithms with <i>m</i> = 80, <i>σ</i> = 0.3 for the artificial network inference.

<p>(a) Comparison of averaged ROC curves. (b) Comparison of averaged PR curves.</p

Comparison of the averaged ROC and PR curves in the MAPK network identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.

<p>(a) Averaged ROC curves. (b) Averaged PR curves.</p

Estimation performances for the artificial linear network.

<p>Estimation performances for the artificial linear network.</p

Reconstruction performance of the SmOMP, SubLM1, SubLM2 and TLS algorithms with <i>m</i> = 1000, <i>σ</i> = 2.0 for the artificial network inference.

<p>(a) Comparison of averaged ROC curves. (b) Comparison of averaged PR curves.</p

Additional file 1 of Insights into the genetic variability and evolutionary dynamics of tomato spotted wilt orthotospovirus in China

Supplementary Material 1: Figure S1. Representative 1.5% agarose gel showing expected amplicons of all three genomic RNAs of YNHH isolate of TSWV. Figure S2. Recombination Analysis Tool (RAT) output for L segment of TSWV: (A) KP008132 (pepper-Spain), (B) MK348942 (pepper-Italy). Figure S3. Recombination Analysis Tool (RAT) output for M segment of newly reported YNKM-2 isolate of TSWV. Figure S4. Recombination Analysis Tool (RAT) output for S segment of TSWV: (A) MG989674 (pepper-Italy), (B) MG989675 (pepper-Italy). Figure S5. Graphical representation of potential reassortment breakpoints detected among mixed TSWV genomic segments: L, M, and S RNA