Imputation for Lipidomics and Metabolomics (ImpLiMet): a web-based application for optimization and method selection for missing data imputation.

Motivation: Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data.

The clinical spectrum of SMA-PME and in vitro normalization of its cellular ceramide profile.

Objective: The objectives of this study were to define the clinical and biochemical spectrum of spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME) and to determine if aberrant cellular ceramide accumulation could be normalized by enzyme replacement.

Methods: Clinical features of 6 patients with SMA-PME were assessed by retrospective chart review, and a literature review of 24 previously published cases was performed. Leukocyte enzyme activity of acid ceramidase was assessed with a fluorescence-based assay.

Predicting glycerophosphoinositol identities in lipidomic datasets using VaLID (Visualization and Phospholipid Identification)--an online bioinformatic search engine.

The capacity to predict and visualize all theoretically possible glycerophospholipid molecular identities present in lipidomic datasets is currently limited. To address this issue, we expanded the search-engine and compositional databases of the online Visualization and Phospholipid Identification (VaLID) bioinformatic tool to include the glycerophosphoinositol superfamily. VaLID v1.

Visualization and Phospholipid Identification (VaLID): online integrated search engine capable of identifying and visualizing glycerophospholipids with given mass.

Motivation: Establishing phospholipid identities in large lipidomic datasets is a labour-intensive process. Where genomics and proteomics capitalize on sequence-based signatures, glycerophospholipids lack easily definable molecular fingerprints. Carbon chain length, degree of unsaturation, linkage, and polar head group identity must be calculated from mass to charge (m/z) ratios under defined mass spectrometry (MS) conditions.