MOTIVATION: Few methods exist for timing individual amplification events in regions of focal amplification. Current methods are also limited in the copy number states that they are able to time. Here we introduce AmplificationTimeR, a method for timing higher level copy number gains and inferring the most parsimonious order of events for regions that have undergone both single gains and whole genome duplication. Our method is an extension of established approaches for timing genomic gains. RESULTS: We can time more copy number states, and in states covered by other methods our results are comparable to previously published methods. AVAILABILITY AND IMPLEMENTATION: AmplificationTimer is freely available as an R package hosted at https://github.com/Wedge-lab/AmplificationTimeR.

To the Editor, The Isaac pipeline described in the Bioinformatics article ‘Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platform’ (Raczy et al., 2013) has been used in cancer sequencing studies (Burns et al., 2018; Quigley et al., 2018) and in the ongoing UK 100 000 Genomes Project (100KGP) (Turnbull et al., 2018). Whilst Isaac has been benchmarked with respect to variant calling (Raczy et al., 2013), there has been less extensive evaluation of its suitability for other analyses routine to cancer genomics. Estimating the fraction of cancer cells with individual somatic mutations is central to cancer genome studies, including characterization of clonal architecture (Dentro et al., 2017). Estimation of these cancer cell fractions (CCFs) is however contingent on unbiased assessment of the fraction of reads supporting variant allele frequencies (VAFs). We demonstrate that VAFs computed by Isaac are biased by the preferential soft clipping of reads supporting non-reference alleles, with deleterious consequences on downstream analyses reliant on unbiased CCF estimation. Reads supporting heterozygous single-nucleotide polymorphism (SNP) reference and alternate alleles can be expected to occur with equal probability when sequencing normal tissue. Due to limited sequencing depth, the exact number of reads supporting reference and alternate alleles will not be equal in many instances, even when the aligner is unbiased. It can be expected however that the median VAF of a large number of heterozygous SNPs will be 0.5. We assessed heterozygous SNP VAF distributions in whole genome sequencing (WGS) data from the germline of 25 multiple myeloma (MM) tumor-normal pairs aligned to GRCh38Decoy assembly using Isaac v03.16.02.19. Germline variants were called using Starling v2.4.7 (Raczy et al., 2013) and VAFs were calculated directly from alignment files using alleleCount (Van Loo et al., 2010). Median VAFs per sample ranged from 0.478 to 0.479 (Fig. 1A), with this consistent skew indicating that Isaac can exhibit bias toward the reference allele. Evidence of reference bias from Isaac. (A) Heterozygous SNP VAF distributions in sequencing data from 25 normal samples. Dashed line represents expected median VAF of 0.5. Whiskers extend 1.5 times inter-quartile range and values outside of this range are not shown. (B) Proportion of reads covering SNP positions supporting the reference (blue line) and alternate (red line) alleles with that read position soft clipped. (C) SNV VAFs from 25 tumor-normal pairs. (D) CCFs of clonal mutation clusters identified by DPClust. Blue and grey dashed lines denote the median putative clonal mutation cluster CCF and a CCF of 1, respectively. (E) Ccube sample purity estimates. Distribution differences assessed using Wilcoxon Signed-Rank test Evidence of reference bias from Isaac. (A) Heterozygous SNP VAF distributions in sequencing data from 25 normal samples. Dashed line represents expected median VAF of 0.5. Whiskers extend 1.5 times inter-quartile range and values outside of this range are not shown. (B) Proportion of reads covering SNP positions supporting the reference (blue line) and alternate (red line) alleles with that read position soft clipped. (C) SNV VAFs from 25 tumor-normal pairs. (D) CCFs of clonal mutation clusters identified by DPClust. Blue and grey dashed lines denote the median putative clonal mutation cluster CCF and a CCF of 1, respectively. (E) Ccube sample purity estimates. Distribution differences assessed using Wilcoxon Signed-Rank test Isaac has a parameter (–clip-semi-aligned) that invokes the soft clipping of reads at each end until a stretch of five consecutive bases are matched with the reference sequence (here, we term this ‘alt-clipping’ to distinguish from soft clipping performed for other reasons). This parameter is present in all Isaac versions after v01.13.06.20 and was used to align the 25 normal samples. To test whether alt-clipping is responsible for the reference bias exhibited by Isaac, we re-aligned the 25 normal samples with Isaac without alt-clipping. When alt-clipping was not performed, the median VAF of heterozygous SNPs in each sample equaled 0.500 (Fig. 1A), thereby showing that alt-clipping introduces reference bias. Alt-clipping results in the clipping of the majority of reads supporting the alternate allele where the variant position is within five bases of either read end (Fig. 1B). Fewer reads supporting the reference allele are soft clipped and VAFs therefore become biased towards the reference allele. If the preferential soft clipping of reads supporting the alternate allele is responsible for the reference bias, then we would expect the effect to be negated if the ends of all reads were soft-clipped by five bases. To further validate the effect of alt-clipping, we therefore soft-clipped five bases at each end of all reads in the alt-clipped alignments (here, we term this ‘balanced-clipping’). The median VAF of heterozygous SNPs in balanced-clipped alignments equaled 0.500 in each sample (Fig. 1A), demonstrating that the preferential clipping of reads supporting the alternate allele introduced by alt-clipping causes reference bias. To assess the effect of alt-clipping on the analysis of cancer genomes, we aligned the tumor MM WGS data using Isaac with and without alt-clipping. Somatic single nucleotide variants (SNVs) were called using Strelka v2.4.7 (Kim et al., 2018). Unlike SNPs in normal samples, we do not know the true VAF of SNVs in tumor samples, as they can be affected by copy number aberration, normal sample contamination and clonal heterogeneity. SNV VAFs from alt-clipped alignments were however lower than SNV VAFs from the same samples from alignments generated without alt-clipping (P < 2.2 × 10−16; Fig. 1C), indicating that alt-clipping also affects somatic SNV VAFs. To test the effect of alt-clipping on subclonal reconstruction, we ran Battenberg and DPClust, which uses a Dirichlet process to model subclonal fractions (Nik-Zainal et al., 2012). If SNV VAFs do not exhibit allelic bias, we would expect DPClust to identify clusters of mutations with CCFs centered on 1, representing clonal mutations. When DPClust was run using alignments generated with alt-clipping, the median CCF of putative clonal mutation clusters (defined as the cluster with a CCF closest to 1) was 0.959, compared to 0.983 when run using alignments generated without alt-clipping (Fig. 1D). Finally, we assessed the effect of alt-clipping-induced reference bias on tumor sample purity estimation. Sample purities estimated using Ccube (Yuan et al., 2018) were smaller when computed using alt-clipped alignments than non-alt-clipped alignments (P = 1.3 × 10−3; Fig. 1E), demonstrating that alt-clipping also affects purity estimation. Reference bias introduced by Isaac through alt-clipping can affect downstream processes, potentially making conclusions unreliable for many types of cancer analysis. If unbiased VAFs are required, Isaac should be run with soft clipping of semi-aligned reads disabled, or an alternative aligner such as BWA (Li and Durbin, 2009) should be used. Although realignment can be performed where clipped alignments have been previously produced, this may be cost or time-prohibited. For example, projects such as 100KGP have already sequenced and aligned >10 000 tumor-normal genome pairs. In such cases, equally clipping all reads would enable downstream analyses reliant on unbiased VAFs without the need for sequencing data realignment. While the Isaac aligner version assessed in this study (v03.16.02.19) was released in April 2016, as of November 2019 it is still being used with reference-bias-introducing alt-clipping in 100KGP. Whether reference bias has affected previous studies using the Isaac aligner is difficult to predict. It is clearly essential that aligners, such as Isaac, be evaluated to ensure that the data they produce are not systematically biased. This work was supported by grants from Cancer Research UK [grant number C1298/A8362], Myeloma UK and a David Forbes Nixon Foundation Fellowship (to M.K.). Conflict of Interest: none declared.

MOTIVATION: Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a 'mutually exclusive' manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive. RESULTS: We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks. AVAILABILITY AND IMPLEMENTATION: BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2011) 27(8), 1108–1112. The above paper was incorrectly published without an Open Access copyright line. The publishers would like to apologize.

MOTIVATION: The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets. RESULTS: Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral molecules and associated molecular formula and matching of the molecular formulae to a reference file of metabolites. The software is independent of the instrument and data pre-processing applied. The number of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of experimental data. AVAILABILITY: The workflows, standard operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html. CONTACT: [email protected].

MOTIVATION: Directed evolution, in addition to its principal application of obtaining novel biomolecules, offers significant potential as a vehicle for obtaining useful information about the topologies of biomolecular fitness landscapes. In this article, we make use of a special type of model of fitness landscapes-based on finite state machines-which can be inferred from directed evolution experiments. Importantly, the model is constructed only from the fitness data and phylogeny, not sequence or structural information, which is often absent. The model, called a landscape state machine (LSM), has already been used successfully in the evolutionary computation literature to model the landscapes of artificial optimization problems. Here, we use the method for the first time to simulate a biological fitness landscape based on experimental evaluation. RESULTS: We demonstrate in this study that LSMs are capable not only of representing the structure of model fitness landscapes such as NK-landscapes, but also the fitness landscape of real DNA oligomers binding to a protein (allophycocyanin), data we derived from experimental evaluations on microarrays. The LSMs prove adept at modelling the progress of evolution as a function of various controlling parameters, as validated by evaluations on the real landscapes. Specifically, the ability of the model to 'predict' optimal mutation rates and other parameters of the evolution is demonstrated. A modification to the standard LSM also proves accurate at predicting the effects of recombination on the evolution.

The main aim of landscape analysis has been to quantify the 'hardness' of problems. Early steps have been made towards extending this into Genetic Programming. However, few attempts have been made to extend the use of landscape analysis into the prediction of ways to make a problem easy, through the optimal setting of control parameters. This paper introduces a new class of landscape metrics, which we call 'Genotype-Fitness Correlations'. An example of this family of metrics is applied to six real-world regression problems. It is demonstrated that genotype-fitness correlations may be used to estimate optimum population sizes for the six problems. We believe that this application of a landscape metric as guidance in the setting of control parameters is an important step towards the development of an adaptive algorithm that can respond to the perceived landscape in 'real-time', i.e. during the evolutionary search process itself.

The accurate quantification of proteins is important in several areas of cell biology, biotechnology and medicine. Both relative and absolute quantification of proteins is often determined following mass spectrometric analysis of one or more of their constituent peptides. However, in order for quantification to be successful, it is important that the experimenter knows which peptides are readily detectable under the mass spectrometric conditions used for analysis. In this paper, genetic programming is used to develop a function which predicts the detectability of peptides from their calculated physico-chemical properties. Classification is carried out in two stages: the selection of a good classifier using the AUROC objective function and the setting of an appropriate threshold. This allows the user to select the balance point between conflicting priorities in an intuitive way. The success of this method is found to be highly dependent on the initial selection of input parameters. The use of brood recombination and a modified version of the multi-objective FOCUS method are also investigated. While neither has a significant effect on predictive accuracy, the use of the FOCUS method leads to considerably more compact solutions.

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria.

The emerging 5 GHz unlicensed bands in Europe and the US may be used to support short range, high rate links for portable computers. This would overcome the range and directionality limitations of today's infrared connections. This paper investigates the performance of a binary FM link in this application, operating in a dispersive indoor channel. The consequence of increasing the modulation index is explored as a cost free means of multipath mitigation. An increase from 0.5 (MSK) to 1 was found to yield a worthwhile improvement in tolerable delay spread. Increasing the index beyond 1 gave no further improvement but significantly affected the spectral occupancy of the signal. This defines a precise design point for high rate, short range radios.