Browsing by Author "Tran, H."
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- A Bayesian Assignment Method for Ambiguous Bisulfite Short ReadsTran, H.; Wu, X.; Tithi, S.; Sun, M.-A.; Xie, H.; Zhang, L. (PLOS, 2016-03-24)DNA methylation is an epigenetic modification critical for normal development and diseases. The determination of genome-wide DNA methylation at single-nucleotide resolution is made possible by sequencing bisulfite treated DNA with next generation high-throughput sequencing. However, aligning bisulfite short reads to a reference genome remains challenging as only a limited proportion of them (around 50–70%) can be aligned uniquely; a significant proportion, known as multireads, are mapped to multiple locations and thus discarded from downstream analyses, causing financial waste and biased methylation inference. To address this issue, we develop a Bayesian model that assigns multireads to their most likely locations based on the posterior probability derived from information hidden in uniquely aligned reads. Analyses of both simulated data and real hairpin bisulfite sequencing data show that our method can effectively assign approximately 70% of the multireads to their best locations with up to 90% accuracy, leading to a significant increase in the overall mapping efficiency. Moreover, the assignment model shows robust performance with low coverage depth, making it particularly attractive considering the prohibitive cost of bisulfite sequencing. Additionally, results show that longer reads help improve the performance of the assignment model. The assignment model is also robust to varying degrees of methylation and varying sequencing error rates. Finally, incorporating prior knowledge on mutation rate and context specific methylation level into the assignment model increases inference accuracy. The assignment model is implemented in the BAM-ABS package and freely available at https://github.com/zhanglabvt/BAM_ABS.
- Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 2. Rumen degradable and undegradable proteinWhite, Robin R.; Roman-Garcia, Y.; Firkins, J. L.; Kononoff, P.; VandeHaar, M. J.; Tran, H.; McGill, Tyler; Garnett, R.; Hanigan, Mark D. (2017-05)This work evaluated the National Research Council (NRC) dairy model (2001) predictions of rumen undegradable (RUP) and degradable (RDP) protein compared with measured postruminal non-ammonia, nonmicrobial (NANMN) and microbial N flows. Models were evaluated using the root mean squared prediction error (RMSPE) as a percent of the observed mean; mean and slope biases as percentages of mean squared prediction error (MSPE), and concordance correlation coefficient, (CCC). The NRC (2001) over-estimated NANMN by 18% and under-estimated microbial N by 14%. Both responses had large mean biases (19% and 20% of MSPE, respectively); and NANMN had a slope Dias (22% of MSPE). The NRC NANMN estimate had high RMSPE (46% of observed mean) and low CCC (0.37); updating feed library A, B, and C protein fractions and degradation rate (K-d) estimates with newer literature only marginally improved fit. The re-fit NRC models for NANMN and microbial N had CCC of 0.89 and 0.94, respectively. When compared with a prediction of NANMN as a static mean fraction of N intake; the re-derived NRC approach did not have improved fit. A protein system of intermediate complexity was derived in an attempt to estimate NANMN with improved fit compared with the static mean NANMN model. In this system, postruminal appearance of A, B, and C protein fractions were predicted in a feed-type specific mariner rather than from estimated passage and degradation rates. In a comparison to independent data. achieved through cross-validation; the new protein system improved RMSPE (34 vs. 36% of observed mean) and CCC (0.42 vs. 0.30) compared with the static mean NANMN model. When the NRC microbial N equation was re-derived, the RDP term dropped from the model. Consequently, 2 new microbial protein equations were formulated, both used a saturating (increasing at a decreasing rate) form: one saturated with respect to TDN and the other saturated over increasing intakes of rumen degraded starch and NDF. Both equations expressed maximal microbial N production as a linear function of RDP intake. The function relating microbial N to intake of rumen degradable carbohydrate improved RMSPE (24 vs. 28% of the observed mean) and CCC (0.63 vs 0.30) compared with the re-derived NRC model. The newly derived equations showed modest improvements in model fit and improved capacity to account for known biological effects; however, substantial variability in NANMN and microbial N estimates remained unexplained.
- Predictions of ruminal outflow of essential amino acids in dairy cattleFleming, A. J.; Lapierre, H.; White, Robin R.; Tran, H.; Kononoff, P. J.; Martineau, R.; Weiss, W. P.; Hanigan, Mark D. (2019-12)The objective of this work was to update and evaluate predictions of essential AA (EAA) outflows from the rumen. The model was constructed based on previously derived equations for rumen-undegradable (RUP), microbial (MiCP), and endogenous (EndCP) protein outflows from the rumen, and revised estimates of ingredient composition and EAA composition of the protein fractions. Corrections were adopted to account for incomplete recovery of EAA during 24-h acid hydrolysis. The predicted ruminal protein and EAA outflows were evaluated against a data set of observed values from the literature. Initial evaluations indicated a minor mean bias for non-ammonia, non-microbial nitrogen flow ([RUP EndCP]/6.25) of 16 g of N per day. Root mean squared errors (RMSE) of EA.A predictions ranged from 26.8 to 40.6% of observed mean values. Concordance correlation coefficients (CCC) of EAA predictions ranged from 0.34 to 0.55. Except for Leu, all ruminal EA.A outflows were overpredicted by 3.0 to 32 g/d. In addition, small but significant slope biases were present for Arg [2.2% mean squared error (MSE)] and Lys (3.2% MSE). The overpredictions may suggest that the mean recovery of AA from acid hydrolysis across laboratories was less than estimates encompassed in the recovery factors. To test this hypothesis, several regression approaches were undertaken to identify potential causes of the bias. These included regressions of (1) residual errors for predicted EAA flows on each of the 3 protein-driven EA flows, (2) observed EAA flows on each protein-driven EAA flow, including an intercept, (3) observed EAA. flows on the protein-driven EAA flows, excluding an intercept term, and (4) observed EAA. flows on IMP and MiCP. However, these equations were deemed unsatisfactory for bias adjustment, as they generated biologically unfeasible predictions for some entities. Future work should focus on identifying the cause of the observed prediction bias.