  Wire-Cell is a 3D event reconstruction package for liquid argon time
projection chambers. Through geometry, time, and drifted charge from multiple
readout wire planes, 3D space points with associated charge are reconstructed
prior to the pattern recognition stage. Pattern recognition techniques,
including track trajectory and $dQ/dx$ (ionization charge per unit length)
fitting, 3D neutrino vertex fitting, track and shower separation,
particle-level clustering, and particle identification are then applied on
these 3D space points as well as the original 2D projection measurements. A
deep neural network is developed to enhance the reconstruction of the neutrino
interaction vertex. Compared to traditional algorithms, the deep neural network
boosts the vertex efficiency by a relative 30\% for charged-current $\nu_e$
interactions. This pattern recognition achieves 80-90\% reconstruction
efficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency for
charged-current $\nu_e$ ($\nu_\mu$) interactions. Based on the resulting
reconstructed particles and their kinematics, we also achieve 15-20\% energy
reconstruction resolutions for charged-current neutrino interactions.
