Won-Seok Lee, Laura Lavery, Maxime Rousseaux, Eric Rutledge, Youjin Jang, Ying-Wooi Wan, Sih- Rong Wu, Wonho Kim, Ismael Al-Ramahi, Smruti Rath, Carolyn Adamski, Vitaliy Bondar, Ambika Tewari, Shirin Soleimani, Samantha Mota, Hari Yalamanchili, Harry Orr, Zhandong Liu, Juan Botas, and Huda Zoghbi. Dual targeting of brain region-specific kinases potentiates neurological rescue in Spinocerebellar ataxia type 1. EMBO J, e106106.

Dharmalingam,P., Mahalingam,R., Yalamanchili,H.K., Weng,T., Karmouty- Quintana,H., Guha,A. and Thandavarayan,R.A., 2021. Emerging roles of alternative cleavage and polyadenylation (APA) in human disease. Journal of cellular physiology, 10.1002/jcp.30549.

Yalamanchili, H.K., Elrod, N.D., Jensen, M.K., Ji, P., Lin, A., Wagner, E.J., and Liu, Z., 2021. A Computational pipeline to infer alternative poly-adenylation from 3’ Sequencing data. Methods in Enzymology 655.

Jensen, M.K., Elrod, N.D., Yalamanchili, H.K., Ji, P., Lin, A., Liu, Z., and Wagner, E.J., 2021. Application and design considerations for 3′-end sequencing using click-chemistry. Methods in Enzymology 655.

Yalamanchili,H.K., Alcott,C.E., Ji,P., Wagner,E.J., Zoghbi,H.Y. and Liu,Z., 2020. PolyA-miner: accurate assessment of differential alternative poly-adenylation from 3′Seq data using vector projections and non-negative matrix factorization. Nucleic Acids Research , 10.1093/nar/gkaa398

Mangleburg, C.G.*, Wu, T.*, Yalamanchili, H.K.*, Guo,C., Hsieh,Y.-C., Duong,D.M., Dammer,E.B., De Jager,P.L., Seyfried,N.T., Liu,Z., et al., 2020 (*Contributed equally). Integrated analysis of the aging brain transcriptome and proteome in tauopathy. Molecular Neurodegeneration, 10.1186/s13024-020-00405-4

Alcott, C.E., Yalamanchili, H.K., Ji, P., van der Heijden, M.E., Saltzman, A.B., Leng, M., Bhatt, B., Hao, S., Wang, Q., Saliba, A., Tang, J., Malovannaya, A., Wagner, E.J., Liu, Z., Zoghbi, H.Y., 2020. Partial loss of CFIm25 causes aberrant alternative polyadenylation and learning deficits. eLife; 9:e50895.

Hsieh, Y.-C.*, Guo, C.*, Yalamanchili, H.K.*, Abreha, M., Al-Ouran, R., Li, Y., Dammer, E.B., Lah, J.J., Levey, A.I., Bennett, D.A., De Jager, P.L., Seyfried, N.T., Liu, Z., Shulman, J.M., 2019 (*Contributed equally). Tau-mediated Disruption of the Spliceosome Triggers Cryptic RNA-splicing and Neurodegeneration in Alzheimer’s Disease. Cell reports, 10.1016/j.celrep.2019.08.104

Zhou, Wenjun, He, Y., Rehman, A.U., Kong, Y., Hong, S., Ding, G., Yalamanchili, H.K., Wan, Y.W., Paul, B., Wang, C., Gong, Y., Zhou, Wenxian, Liu, H., Dean, J., Scalais, E., O’Driscoll, M., Morton, J.E.V., Hou, X., Wu, Q., Tong, Q., Liu, Z., Liu, P., Xu, Y., Sun, Z., 2019. Loss of function of NCOR1 and NCOR2 impairs memory through a novel GABAergic hypothalamus–CA3 projection. Nature Neuroscience, 10.1038/s41593-018-0311-1.

De Maio, A.*, Yalamanchili, H.K.*, Adamski, C.J., Gennarino, V.A., Liu, Z., Qin, J., Jung, S.Y., Richman, R., Orr, H., Zoghbi, H.Y., 2018 (*Contributed equally). RBM17 Interacts with U2SURP and CHERP to Regulate Expression and Splicing of RNA-Processing Proteins. Cell Reports, 10.1016/j.celrep.2018.09.041.

Ito-Ishida, A.*, Yamalanchili, H.K.*, Shao, Y., Baker, S.A., Heckman, L.D., Lavery, L.A., Kim, J.Y., Lombardi, L.M., Sun, Y., Liu, Z., Zoghbi, H.Y., 2018 (*Contributed equally). Genome-wide distribution of linker histone H1.0 is independent of MeCP2. Nature Neuroscience, 10.1038/s41593-018-0155-8.

Pohodich, A.E., Yalamanchili, H., Raman, A.T., Wan, Y.W., Gundry, M., Hao, S., Jin, H., Tang, J., Liu, Z., Zoghbi, H.Y., 2018. Forniceal deep brain stimulation induces gene expression and splicing changes that promote neurogenesis and plasticity. eLife, 10.7554/eLife.34031.

Jeong, H.H., Yalamanchili, H.K., Guo, C., Shulman, J.M., Liu, Z., 2018. An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data, in: Pacific Symposium on Biocomputing.

Raman, A.T., Pohodich, A.E., Wan, Y.W., Yalamanchili, H.K., Lowry, W.E., Zoghbi, H.Y., Liu, Z., 2018. Apparent bias toward long gene misregulation in MeCP2 syndromes disappears after controlling for baseline variations. Nature Communications, 10.1038/s41467-018-05627-1.

Yalamanchili HK*, Wan YW*, Liu Z., 2017 (*Contributed equally). Data Analysis Pipeline for RNA-seq Experiments: From Differential Expression to Cryptic Splicing.Current Protocols in Bioinformatics , 10.1002/cpbi.33.

Lo, Y.H., Chung, E., Li, Z., Wan, Y.W., Mahe, M.M., Chen, M.S., Noah, T.K., Bell, K.N., Yalamanchili, H.K., Klisch, T.J., Liu, Z., Park, J.S., Shroyer, N.F., 2017. Transcriptional Regulation by ATOH1 and its Target SPDEF in the Intestine. CMGH, 10.1016/j.jcmgh.2016.10.001.

Tan, Q.*, Yalamanchili, H.K.,* Park, J.*, De Maio, A., Lu, H.C., Wan, Y.W., White, J.J., Bondar, V. V., Sayegh, L.S., Liu, X., Gao, Y., Sillitoe, R. V., Orr, H.T., Liu, Z., Zoghbi, H.Y., 2016 (*Contributed equally). Extensive cryptic splicing upon loss of RBM17 and TDP43 in neurodegeneration models. Human Molecular Genetics, 10.1093/hmg/ddw337.

Qin, Y., Yalamanchili, H.K., Qin, J., Yan, B., Wang, J., 2015. The current status and challenges in computational analysis of genomic big data. Big Data Research, 10.1016/j.bdr.2015.02.005.

Hu, J., Zhao, Z., Yalamanchili, H.K., Wang, J., Ye, K., Fan, X., 2015. Bayesian detection of embryonic gene expression onset in C. elegans. Annals of Applied Statistics, 10.1214/15-AOAS820.

Dineshram, R., Quan, Q., Sharma, R., Chandramouli, K., Yalamanchili, H.K., Chu, I., Thiyagarajan, V., 2015. Comparative and quantitative proteomics reveal the adaptive strategies of oyster larvae to ocean acidification. Proteomics, 10.1002/pmic.201500198.

Yalamanchili, H.K., Li, Z., Wang, P., Wong, M.P., Yao, J., Wang, J., 2014. SpliceNet: Recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples. Nucleic Acids Research, 10.1093/nar/gku577.

Yalamanchili, H.K., Yan, B., Li, M.J., Qin, J., Zhao, Z., Chin, F.Y.L., Wang, J., 2014. DDGni: Dynamic delay gene-network inference from high-temporal data using gapped local alignment. Bioinformatics, 10.1093/bioinformatics/btt692.

Qin, J., Hu, Y., Xu, F., Yalamanchili, H.K., Wang, J., 2014. Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods. Methods, 10.1016/j.ymeth.2014.03.006.

Wang, P., Lai, W.F., Li, M.J., Xu, F., Yalamanchili, H.K., Lovell-Badge, R., Wang, J., 2013. Inference of Gene-Phenotype Associations via Protein-Protein Interaction and Orthology. PLoS ONE, 10.1371/journal.pone.0077478.

Yalamanchili, H.K., Xiao, Q.W., Wang, J., 2012. A novel neural response algorithm for protein function prediction. BMC Systems Biology, 10.1186/1752-0509-6-S1-S19.

Yang, S., Yalamanchili, H.K., Li, X., Yao, K.M., Sham, P.C., Zhang, M.Q., Wang, J., 2011. Correlated evolution of transcription factors and their binding sites. Bioinformatics, 10.1093/bioinformatics/btr503.

Wu, H.J., Wu, W., Sun, H.Y., Qin, G.W., Wang, H.B., Wang, P., Yalamanchili, H.K., Wang, J., Tse, H.F., Lau, C.P., Vanhoutte, P.M., Li, G.R., 2011. Acacetin causes a frequency- and use-dependent blockade of hKv1.5 channels by binding to the S6 domain. Journal of Molecular and Cellular Cardiology, 10.1016/j.yjmcc.2011.08.022.

Yalamanchili HK, Nita Parekh, BICoB 2009. Graph Spectral Approach for Identifying Protein Domains. Proceedings of International Conference on Bioinformatics and Computational Biology, LNCS 437-448.

Ruchi Jain, Yalamanchili HK, Nita Parekh, NaBIC 2009. Identifying Structural Repeats in Proteins using Graph Centrality Measures. World Congress on Nature & Biologically Inspired Computing (NaBIC), 110-115.

Marla S*, Yalamanchili HK, Gelli P, Singh HK, Praveen G, Srikanth S, Goutham K, 2006. Comparative structure analysis of Chorismate synthase, Online J Bioinformatics, 7, 35-45.