The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness.

PURPOSE: To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness. MATERIALS AND METHODS: Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (Ktrans , ve , kep [TM&SSM] and intracellular water molecule lifetime τi [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores. RESULTS: DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: Ktrans +kep 0.76, SSM: τi +kep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: Ktrans +τi SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213). CONCLUSION: SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma.

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge.

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as Ktrans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T1 were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 for kepvs. 0.74 for Ktrans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Hepatocellular carcinoma: IVIM diffusion quantification for prediction of tumor necrosis compared to enhancement ratios.

PURPOSE: To correlate intra voxel incoherent motion (IVIM) diffusion parameters of liver parenchyma and hepatocellular carcinoma (HCC) with degree of liver/tumor enhancement and necrosis; and to assess the diagnostic performance of diffusion parameters vs. enhancement ratios (ER) for prediction of complete tumor necrosis. PATIENTS AND METHODS: In this IRB approved HIPAA compliant study, we included 46 patients with HCC who underwent IVIM diffusion-weighted (DW) MRI in addition to routine sequences at 3.0 T. True diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (PF) and apparent diffusion coefficient (ADC) were quantified in tumors and liver parenchyma. Tumor ER were calculated using contrast-enhanced imaging, and degree of tumor necrosis was assessed using post-contrast image subtraction. IVIM parameters and ER were compared between HCC and background liver and between necrotic and viable tumor components. ROC analysis for prediction of complete tumor necrosis was performed. RESULTS: 79 HCCs were assessed (mean size 2.5 cm). D, PF and ADC were significantly higher in HCC vs. liver (p < 0.0001). There were weak significant negative/positive correlations between D/PF and ER, and significant correlations between D/PF/ADC and tumor necrosis (for D, r 0.452, p < 0.001). Among diffusion parameters, D had the highest area under the curve (AUC 0.811) for predicting complete tumor necrosis. ER outperformed diffusion parameters for prediction of complete tumor necrosis (AUC > 0.95, p < 0.002). CONCLUSION: D has a reasonable diagnostic performance for predicting complete tumor necrosis, however lower than that of contrast-enhanced imaging.

Non-invasive prediction of portal pressures using CT and MRI in chronic liver disease.

PURPOSE: To assess the diagnostic value of a fast scoring system based on non-invasive cross-sectional imaging to predict portal hypertension (PH) in patients with liver disease. METHODS: In this retrospective study, we included patients who underwent contrast-enhanced CT or MRI within 3 months of hepatic venous pressure gradient (HVPG) measurements. Two independent observers provided an imaging-based scoring system (max of 9): number of variceal sites, volume of ascites, and spleen size. ROC analysis was performed to predict the presence of PH (HVPG ≥ 5 mmHg) and clinically significant PH (HVPG ≥ 10 mmHg). RESULTS: Our cohort consists of 143 patients with mean HVPG of 13.1 ± 2.0 mmHg. Mean PH scores from the two observers were 3.9 ± 2.7 and 3.2 ± 2.5. There was a significant correlation between PH score and HVPG (r = 0.58, p < 0.001 for both observers) with high inter-observer agreement (kappa 0.71). AUCs of 0.78-0.76 and 0.83-0.81 were observed for diagnosing HVPG ≥ 5 mmHg and HVPG ≥ 10 mmHg, respectively, for observers 1 and 2. CONCLUSIONS: We have developed a fast PH imaging-based composite score, which could be used for non-invasive detection of clinically significant PH.

Prospective comparison of magnetic resonance imaging to transient elastography and serum markers for liver fibrosis detection.

BACKGROUND & AIMS: Establishing accurate non-invasive methods of liver fibrosis quantification remains a major unmet need. Here, we assessed the diagnostic value of a multiparametric magnetic resonance imaging (MRI) protocol including diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI and magnetic resonance elastography (MRE) in comparison with transient elastography (TE) and blood tests [including ELF (Enhanced Liver Fibrosis) and APRI] for liver fibrosis detection. METHODS: In this single centre cross-sectional study, we prospectively enrolled 60 subjects with liver disease who underwent multiparametric MRI (DWI, DCE-MRI and MRE), TE and blood tests. Correlation was assessed between non-invasive modalities and histopathologic findings including stage, grade and collagen content, while accounting for covariates such as age, sex, BMI, HCV status and MRI-derived fat and iron content. ROC curve analysis evaluated the performance of each technique for detection of moderate-to-advanced liver fibrosis (F2-F4) and advanced fibrosis (F3-F4). RESULTS: Magnetic resonance elastography provided the strongest correlation with fibrosis stage (r = 0.66, P < 0.001), inflammation grade (r = 0.52, P < 0.001) and collagen content (r = 0.53, P = 0.036). For detection of moderate-to-advanced fibrosis (F2-F4), AUCs were 0.78, 0.82, 0.72, 0.79, 0.71 for MRE, TE, DCE-MRI, DWI and APRI, respectively. For detection of advanced fibrosis (F3-F4), AUCs were 0.94, 0.77, 0.79, 0.79 and 0.70, respectively. CONCLUSIONS: Magnetic resonance elastography provides the highest correlation with histopathologic markers and yields high diagnostic performance for detection of advanced liver fibrosis and cirrhosis, compared to DWI, DCE-MRI, TE and serum markers.

DCE-MRI of hepatocellular carcinoma: perfusion quantification with Tofts model versus shutter-speed model--initial experience.

OBJECTIVE: To quantify hepatocellular carcinoma (HCC) perfusion and flow with the fast exchange regime-allowed Shutter-Speed model (SSM) compared to the Tofts model (TM). MATERIALS AND METHODS: In this prospective study, 25 patients with HCC underwent DCE-MRI. ROIs were placed in liver parenchyma, portal vein, aorta and HCC lesions. Signal intensities were analyzed employing dual-input TM and SSM models. ART (arterial fraction), K (trans) (contrast agent transfer rate constant from plasma to extravascular extracellular space), ve (extravascular extracellular volume fraction), kep (contrast agent intravasation rate constant), and τi (mean intracellular water molecule lifetime) were compared between liver parenchyma and HCC, and ART, K (trans), v e and k ep were compared between models using Wilcoxon tests and limits of agreement. Test-retest reproducibility was assessed in 10 patients. RESULTS: ART and v e obtained with TM; ART, ve, ke and τi obtained with SSM were significantly different between liver parenchyma and HCC (p < 0.04). Parameters showed variable reproducibility (CV range 14.7-66.5% for both models). Liver K (trans) and ve; HCC ve and kep were significantly different when estimated with the two models (p < 0.03). CONCLUSION: Our results show differences when computed between the TM and the SSM. However, these differences are smaller than parameter reproducibilities and may be of limited clinical significance.

Simultaneous measurement of hepatic and splenic stiffness using MR elastography: preliminary experience.

PURPOSE: To compare MR elastography (MRE) using a single and a dual driver excitation for the quantification of hepatic and splenic stiffness (HS and SS), and to investigate the performance of HS and SS measured with single or dual driver excitation for the detection of liver cirrhosis in subjects with liver disease. PATIENTS AND METHODS: This prospective HIPAA compliant and IRB approved study involved 49 subjects who underwent MRE at 3.0T, comparing three different acquisition methods (single driver on the liver, single driver on the spleen and dual driver acoustic excitation). A Mann-Whitney test was used to assess changes in stiffness values. Bland-Altman analysis was used to compare single and dual driver configurations for each organ. Performance for detection of liver cirrhosis was assessed using ROC analysis. Pearson correlation was used to estimate the dependence of HS and SS on spleen size. RESULTS: There were 40 noncirrhotic and 9 cirrhotic patients. There was good agreement between stiffness values measured with a single or a dual driver (Bland-Altman limits of agreement -14.3 % to 18.9 % and -18.1 % to 29.7 %, CV 6.4 % and 9.4 %, for HS and SS. respectively). HS and SS were higher in subjects with liver cirrhosis (p < 0.001), with excellent detection performance (AUROC range 0.87-0.93). SS correlated strongly with spleen size (r = 0.69, p < 0.001), while HS showed weak correlation (r = 0.38, p = 0.006). CONCLUSION: Using a dual acoustic driver configuration, hepatic and splenic stiffness can be simultaneously estimated with good concordance with single driver measurement.

DCE-MRI of the liver: reconstruction of the arterial input function using a low dose pre-bolus contrast injection.

PURPOSE: To assess the quality of the arterial input function (AIF) reconstructed using a dedicated pre-bolus low-dose contrast material injection imaged with a high temporal resolution and the resulting estimated liver perfusion parameters. MATERIALS AND METHODS: In this IRB-approved prospective study, 24 DCE-MRI examinations were performed in 21 patients with liver disease (M/F 17/4, mean age 56 y). The examination consisted of 1.3 mL and 0.05 mmol/kg of gadobenate dimeglumine for pre-bolus and main bolus acquisitions, respectively. The concentration-curve of the abdominal aorta in the pre-bolus acquisition was used to reconstruct the AIF. AIF quality and shape parameters obtained with pre-bolus and main bolus acquisitions and the resulting estimated hepatic perfusion parameters obtained with a dual-input single compartment model were compared between the 2 methods. Test-retest reproducibility of perfusion parameters were assessed in three patients. RESULTS: The quality of the pre-bolus AIF curve was significantly better than that of main bolus AIF. Shape parameters peak concentration, area under the time activity curve of gadolinium contrast at 60 s and upslope of pre-bolus AIF were all significantly higher, while full width at half maximum was significantly lower than shape parameters of main bolus AIF. Improved liver perfusion parameter reproducibility was observed using pre-bolus acquisition [coefficient of variation (CV) of 4.2%-38.7% for pre-bolus vs. 12.1-71.4% for main bolus] with the exception of distribution volume (CV of 23.6% for pre-bolus vs. 15.8% for main bolus). The CVs between pre-bolus and main bolus for the perfusion parameters were lower than 14%. CONCLUSION: The AIF reconstructed with pre-bolus low dose contrast injection displays better quality and shape parameters and enables improved liver perfusion parameter reproducibility, although the resulting liver perfusion parameters demonstrated no clinically significant differences between pre-bolus and main bolus acquisitions.

DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility.

PURPOSE: To evaluate the effect of different methods to convert magnetic resonance (MR) signal intensity (SI) to gadolinium concentration ([Gd]) on estimation and reproducibility of model-free and modeled hepatic perfusion parameters measured with dynamic contrast-enhanced (DCE)-MRI. MATERIALS AND METHODS: In this Institutional Review Board (IRB)-approved prospective study, 23 DCE-MRI examinations of the liver were performed on 17 patients. SI was converted to [Gd] using linearity vs. nonlinearity assumptions (using spoiled gradient recalled echo [SPGR] signal equations). The [Gd] vs. time curves were analyzed using model-free parameters and a dual-input single compartment model. Perfusion parameters obtained with the two conversion methods were compared using paired Wilcoxon test. Test-retest and interobserver reproducibility of perfusion parameters were assessed in six patients. RESULTS: There were significant differences between the two conversion methods for the following parameters: AUC60 (area under the curve at 60 s, P < 0.001), peak gadolinium concentration (Cpeak, P < 0.001), upslope (P < 0.001), Fp (portal flow, P = 0.04), total hepatic flow (Ft, P = 0.007), and MTT (mean transit time, P < 0.001). Our preliminary results showed acceptable to good reproducibility for all model-free parameters for both methods (mean coefficient of variation [CV] range, 11.87-23.7%), except for upslope (CV = 37%). Among modeled parameters, DV (distribution volume) had CV <22% with both methods, PV and MTT showed CV <21% and <29% using SPGR equations, respectively. Other modeled parameters had CV >30% with both methods. CONCLUSION: Linearity assumption is acceptable for quantification of model-free hepatic perfusion parameters while the use of SPGR equations and T1 mapping may be recommended for the quantification of modeled hepatic perfusion parameters.

Quantitative liver MRI combining phase contrast imaging, elastography, and DWI: assessment of reproducibility and postprandial effect at 3.0 T.

PURPOSE: To quantify short-term reproducibility (in fasting conditions) and postprandial changes after a meal in portal vein (PV) flow parameters measured with phase contrast (PC) imaging, liver diffusion parameters measured with multiple b value diffusion-weighted imaging (DWI) and liver stiffness (LS) measured with MR elastography (MRE) in healthy volunteers and patients with liver disease at 3.0 T. MATERIALS AND METHODS: In this IRB-approved prospective study, 30 subjects (11 healthy volunteers and 19 liver disease patients; 23 males, 7 females; mean age 46.5 y) were enrolled. Imaging included 2D PC imaging, multiple b value DWI and MRE. Subjects were initially scanned twice in fasting state to assess short-term parameter reproducibility, and then scanned 20 min. after a liquid meal. PV flow/velocity, LS, liver true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (PF) and apparent diffusion coefficient (ADC) were measured in fasting and postprandial conditions. Short-term reproducibility was assessed in fasting conditions by measuring coefficients of variation (CV) and Bland-Altman limits of agreement. Differences in MR metrics before and after caloric intake and between healthy volunteers and liver disease patients were assessed. RESULTS: PV flow parameters, D, ADC and LS showed good to excellent short-term reproducibility in fasting state (CV <16%), while PF and D* showed acceptable and poor reproducibility (CV = 20.4% and 51.6%, respectively). PV flow parameters and LS were significantly higher (p<0.04) in postprandial state while liver diffusion parameters showed no significant change (p>0.2). LS was significantly higher in liver disease patients compared to healthy volunteers both in fasting and postprandial conditions (p<0.001). Changes in LS were significantly correlated with changes in PV flow (Spearman rho = 0.48, p = 0.013). CONCLUSIONS: Caloric intake had no/minimal/large impact on diffusion/stiffness/portal vein flow, respectively. PC MRI and MRE but not DWI should be performed in controlled fasting state.

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Maximum-parsimony haplotype frequencies inference based on a joint constrained sparse representation of pooled DNA.

BACKGROUND: DNA pooling constitutes a cost effective alternative in genome wide association studies. In DNA pooling, equimolar amounts of DNA from different individuals are mixed into one sample and the frequency of each allele in each position is observed in a single genotype experiment. The identification of haplotype frequencies from pooled data in addition to single locus analysis is of separate interest within these studies as haplotypes could increase statistical power and provide additional insight. RESULTS: We developed a method for maximum-parsimony haplotype frequency estimation from pooled DNA data based on the sparse representation of the DNA pools in a dictionary of haplotypes. Extensions to scenarios where data is noisy or even missing are also presented. The resulting method is first applied to simulated data based on the haplotypes and their associated frequencies of the AGT gene. We further evaluate our methodology on datasets consisting of SNPs from the first 7Mb of the HapMap CEU population. Noise and missing data were further introduced in the datasets in order to test the extensions of the proposed method. Both HIPPO and HAPLOPOOL were also applied to these datasets to compare performances. CONCLUSIONS: We evaluate our methodology on scenarios where pooling is more efficient relative to individual genotyping; that is, in datasets that contain pools with a small number of individuals. We show that in such scenarios our methodology outperforms state-of-the-art methods such as HIPPO and HAPLOPOOL.

Maximum parsimony xor haplotyping by sparse dictionary selection.

BACKGROUND: Xor-genotype is a cost-effective alternative to the genotype sequence of an individual. Recent methods developed for haplotype inference have aimed at finding the solution based on xor-genotype data. Given the xor-genotypes of a group of unrelated individuals, it is possible to infer the haplotype pairs for each individual with the aid of a small number of regular genotypes. RESULTS: We propose a framework of maximum parsimony inference of haplotypes based on the search of a sparse dictionary, and we present a greedy method that can effectively infer the haplotype pairs given a set of xor-genotypes augmented by a small number of regular genotypes. We test the performance of the proposed approach on synthetic data sets with different number of individuals and SNPs, and compare the performances with the state-of-the-art xor-haplotyping methods PPXH and XOR-HAPLOGEN. CONCLUSIONS: Experimental results show good inference qualities for the proposed method under all circumstances, especially on large data sets. Results on a real database, CFTR, also demonstrate significantly better performance. The proposed algorithm is also capable of finding accurate solutions with missing data and/or typing errors.

Designing DNA barcodes orthogonal in melting temperature by simulated annealing optimization.

Molecular barcode arrays are widely employed in the analysis of large strain libraries, whereby probes linked to unique oligonucleotides ("antitags") are used to detect selected DNA targets ("tags") by highly specific hybridization. One of the major problems for such screen designs is thus insuring a high degree of probe-target specificity and a low level of nonspecific binding (in sum, "orthogonality") across the entire tag population ("collection"). Several approaches have been previously proposed for designing orthogonal DNA tags by-among others-focusing on their individual or pair-wise structures, such as Smith Waterman sequence similarity, the widely used nearest neighbor method, and full thermodynamic estimates of sequences. However, these methods generally involve imposing various heuristic constraints ("design rules") on possible tag/antitag (TaT) sequences in order to achieve probe-target specificity across the collection. The resulting lack of freedom in considering all putative sequences can lead to potentially suboptimal designs and to the ensuing reduction in the degree of orthogonality within the constructed TaT collections. Here, we demonstrate that a randomized-search algorithm based on simulated annealing optimization can be used in order to substantially free the design process from the limitations of sequence constraints-allowing for the elucidation of potentially more optimal DNA tag collections. The designed sets of DNA oligonucleotides are optimized for the highest degree of orthogonality as quantified by melting temperature Tm-an experimentally relevant system property, which could also be used as a theoretically meaningful thermodynamic metric for optimizing TaT binding specificity. That is, this work describes an approach to constructing tag/antitag libraries, which offer the greatest melting temperature separation between specific probe-target duplexes and other nonspecific structures. The proposed method finds, with high probability, the global solution that maximizes the difference in Tm between the specific and nonspecific tag-antitag hybridizations across a collection of given size for TaTs of specified length. An application of this approach is demonstrated using 2 different DNA probe sets.

Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites.

Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems. However, achieving high discovery performance without imposing substantial auxiliary constraints on possible motif features remains a key algorithmic challenge. This work describes BAMBI-a sequential Monte Carlo motif-identification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such motif properties as length, logo, number of instances and their locations solely on the basis of primary nucleotide sequence data. Furthermore, should biologically meaningful information about motif attributes be available, BAMBI takes advantage of this knowledge to further refine the discovery results. In practical applications, we show that the proposed approach can be used to find sites of such diverse DNA-binding molecules as the cAMP receptor protein (CRP) and Din-family site-specific serine recombinases. Results obtained by BAMBI in these and other settings demonstrate better statistical performance than any of the four widely-used profile-based motif discovery methods: MEME, BioProspector with BioOptimizer, SeSiMCMC and Motif Sampler as measured by the nucleotide-level correlation coefficient. Additionally, in the case of Din-family recombinase target site discovery, the BAMBI-inferred motif is found to be the only one functionally accurate from the underlying biochemical mechanism standpoint. C++ and Matlab code is available at http://www.ee.columbia.edu/~guido/BAMBI or http://genomics.lbl.gov/BAMBI/.