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Recent studies have shown that gene regulation is far more complex than previously believed and does not completely explain changes at the protein level. Therefore, the direct study of the proteome, considerably different in both complexity and dynamicity to the genome/transcriptome, has provided unique insights to an increasing number of researchers. During the past decade, extraordinary advances in proteomic techniques have changed the way we can analyze the composition, regulation, and function of protein complexes and pathways underlying altered neurobiological conditions. When combined with complementary approaches, these advances provide the contextual information for decoding large data sets into meaningful biologically adaptive processes. Neuroproteomics offers potential breakthroughs in the field of alcohol research by leading to a deeper understanding of how alcohol globally affects protein structure, function, interactions, and networks. The wealth of information gained from these advances can help pinpoint relevant biomarkers for early diagnosis and improved prognosis of alcoholism and identify future pharmacological targets for the treatment of this addiction. [ABSTRACT FROM AUTHOR]

Background: RNA-seq is a next generation sequencing method with a wide range of applications including single nucleotide polymorphism (SNP) detection, splice junction identification, and gene expression level measurement. However, the RNA-seq sequence data can be biased during library constructions resulting in incorrect data for SNP, splice junction, and gene expression studies. Here, we developed new library preparation methods to limit such biases. Results: A whole transcriptome library prepared for the SOLiD system displayed numerous read duplications (pileups) and gaps in known exons. The pile-ups and gaps of the whole transcriptome library caused a loss of SNP and splice junction information and reduced the quality of gene expression results. Further, we found clear sequence biases for both 5' and 3' end reads in the whole transcriptome library. To remove this bias, RNaseIII fragmentation was replaced with heat fragmentation. For adaptor ligation, T4 Polynucleotide Kinase (T4PNK) was used following heat fragmentation. However, its kinase and phosphatase activities introduced additional sequence biases. To minimize them, we used OptiKinase before T4PNK. Our study further revealed the specific target sequences of RNaseIII and T4PNK. Conclusions: Our results suggest that the heat fragmentation removed the RNaseIII sequence bias and significantly reduced the pile-ups and gaps. OptiKinase minimized the T4PNK sequence biases and removed most of the remaining pile-ups and gaps, thus maximizing the quality of RNA-seq data. [ABSTRACT FROM AUTHOR]